Data processing method and apparatus

ABSTRACT

A method of compression is disclosed in which an input sequence of bits is divided into a plurality of portions. Each portion is sub-divided into a plurality of sub-divisions. Frequency analysis is performed to determine the number of occurrences of each sub-division permutation and new values are assigned, based on the frequency analysis, to each of the sub-division permutations. For each portion a label representing the permutation of bits in that portion is assigned. The label comprises a representation of a combined value resulting from combining the new values associated with the sub-division permutations of that portion. A processed sequence of bits is generated by replacing, within the input sequence of bits, bit portions with the respective label representing the permutation of bits in that portion.

RELATED APPLICATIONS

This is a Continuation of U.S. patent application Ser. No. 15/741,508filed Jan. 3, 2018, which is a National Phase of PCT Patent ApplicationNo. PCT/GB2016/052020 having International filing date of Jul. 4, 2016,which claims the benefit of priority of United Kingdom PatentApplication No. 1511740.1 filed on Jul. 3, 2015. The contents of theabove applications are all incorporated by reference as if fully setforth herein in their entirety.

FIELD AND BACKGROUND OF THE INVENTION

The present invention relates to a method and apparatus of processingdata, in particular for compressing (and/or encrypting) data.

Currently, information held on a computer is stored as ones and zeros(bits) which are grouped into sets of eight bits which are referred toas bytes. Two bytes are referred to as a word (16 bits), and four bytesare referred to as a double word (32 bits) or can be used as themathematical storage referred to as a 32-bit integer (int32 or Long). Aninteger which has a bit length of 32 can hold a value between−2147483648 and +2147483647; or by removing the sign and making it anunsigned 32-bit integer (UInt32), the longest number that can be storedis 4294967295 (2³²−1).

It is desirable to represent information using the smallest number ofbits possible in order to reduce the space required for storage and tominimise the resources required for signalling information from oneentity to another. In computer science and information theory, datacompression (also referred to as source coding) involves encodinginformation using fewer bits than the original representation.Furthermore, it is important that sensitive data, represented using theAmerican Standard Code Information Interchange (ASCII) standard or byother means, is protected, for example by preventing access to this databy unauthorised persons or machines. Therefore, methods of encryptingand decrypting data form an integral part of information technology.

Compression can be either lossy or lossless. Lossless compressionreduces bits by identifying and eliminating statistical redundancy. Noinformation is lost in lossless compression. In contrast, lossycompression reduces the total number of bits by identifying marginallyimportant information and removing it.

Once data has been compressed, it must subsequently decompressed inorder for it to be used. Both compression and decompression requirecomputer processing. Therefore, data compression/decompression must finda compromise between the level of compression achieved and the computerprocessing required for compression and decompression. For example, acompression scheme for video may require expensive hardware for thevideo to be decompressed fast enough for it to be watched as it is beingdecompressed, and the option to decompress the video in full beforewatching it may be inconvenient and may require additional storage.

SUMMARY OF THE INVENTION

The present invention seeks to provide improved methods of compressionand/or decompression and/or improved methods of encryption and/ordecryption.

According to one aspect of the invention there is provided a method ofprocessing data comprising an input sequence of bits, the methodcomprising the steps of: (i) identifying a processing bit length for usein processing said input sequence of bits; (ii) dividing the inputsequence of bits into a plurality of portions wherein each portion has arespective portion bit length equal to said processing bit length andwherein the bits in each portion are arranged in a respective portionpermutation; (iii) respectively sub-dividing each portion into aplurality of sub-divisions comprising at least a first sub-division anda second sub-division, wherein each sub-division of the plurality ofsub-divisions comprises at least one bit, wherein the at least one bitof each first sub-division is arranged in a respective firstsub-division permutation, and wherein the at least one bit of eachsecond sub-division is arranged in a respective second sub-divisionpermutation; (iv) performing frequency analysis: to determine, for eachof a plurality of possible first sub-division permutations, how manytimes, within said input sequence of bits, a portion comprises a firstsub-division having bits arranged in that possible first sub-divisionpermutation; and to determine, for each of a plurality of possiblesecond sub-division permutations, how many times, within said inputsequence of bits, a portion comprises a second sub-division having bitsarranged in that possible second sub-division permutation; (v)assigning, based on said frequency analysis, a first respectivesub-division value to each of said plurality of possible firstsub-division permutations and assigning a second respective sub-divisionvalue to each of said plurality of possible second sub-divisionpermutations; (vi) for each portion permutation of a plurality ofpossible portion permutations, generating a respective labelrepresenting that portion permutation, wherein said generating comprisescombining: the first sub-division value assigned to the firstsub-division permutation corresponding to the first sub-division of thatportion permutation; with the second sub-division value assigned to thesecond sub-division permutation corresponding to the second sub-divisionof that portion permutation; wherein said respective label comprises arepresentation of a combined value resulting from said combining; and(vii) forming a processed sequence of bits by replacing, within saidinput sequence of bits, bit portions comprising bits arranged in one ofsaid plurality of possible portion permutations, with the respectivelabel representing that one of said plurality of possible portionpermutations.

When generating, for each portion permutation, a respective labelrepresenting that portion permutation, said combining may comprisearithmetically adding said first sub-division value assigned to thefirst sub-division permutation corresponding to the first sub-divisionof that portion permutation to said second sub-division value assignedto the second sub-division permutation corresponding to the secondsub-division of that portion permutation. The combined value may thencomprise a result of the addition.

When generating, for each portion permutation, a respective labelrepresenting that portion permutation, said generating may comprise,when a particular first sub-division value is assigned for a pluralityof different first sub-division permutations), generating, for each ofsaid respective plurality of different first sub-division permutationshaving that particular first sub-division value, a different respectivefirst additional value for use in discriminating between said respectiveplurality of first sub-division permutations having that particularfirst sub-division value.

When generating, for each portion permutation, a respective labelrepresenting that portion permutation, said generating may comprise,when a particular second sub-division value is to be assigned for aplurality of different second sub-division permutations, generating, foreach of said respective plurality of different second sub-divisionpermutations having that particular second sub-division value, adifferent respective second additional value for use in discriminatingbetween said respective plurality of second sub-division permutationshaving that particular second sub-division value.

When generating, for each portion permutation, a respective labelrepresenting that portion permutation, said generating may comprise,when a first additional value and a second additional value have beengenerated for a particular portion permutation: combining said firstadditional value and said second additional value to produce a combinedadditional value, wherein the label for that particular portionpermutation comprises a representation of the combined value togetherwith the combined additional value for that particular portionpermutation.

When generating, for each portion permutation, a respective labelrepresenting that portion permutation, said generating may comprise,when one of a first additional value and a second additional value havebeen generated for a particular portion permutation, generating a labelfor that particular portion permutation that comprises a representationof the combined value together with that one of a first additional valueand a second additional value.

When respectively sub-dividing each portion into a plurality ofsub-divisions, said first sub-division may have a different number ofbits to said second sub-division.

When generating, for each portion permutation, a respective labelrepresenting that portion permutation, each label generated may have arespective label bit length, and the labels are generated such thatlabels generated for portion permutations which occur a greater numberof times within said input sequence of bits may generally have a smallerlabel bit length than labels generated for portion permutations whichoccur a lesser number of times within said input sequence of bits.

When generating, for each portion permutation, a respective labelrepresenting that portion permutation, each label generated may have arespective label bit length, and the labels are generated such that atleast some of the labels may have a label bit length which may besmaller than the processing bit length.

The frequency analysis may comprise, for each one of said plurality ofpossible first sub-division permutations, determining a respectiveoccurrence level which is the number of times, within said sequence ofbits, that a portion occurs comprising that one of said plurality ofpossible first sub-division permutations. The frequency analysis maycomprise may comprise, for each one of said plurality of possible secondsub-division permutations, determining a respective occurrence levelwhich is the number of times, within said sequence of bits, a portionoccurs comprising that one of said plurality of possible secondsub-division permutations.

For a given first sub-division value, the number of first sub-divisionpermutations which are assigned the given first sub-division value maydepend on the occurrence levels associated with the first sub-divisionpermutations which are assigned the given first sub-division value; andfor a given second sub-division value, the number of second sub-divisionpermutations which are assigned the given second sub-division value maydepend on the occurrence levels associated with the second sub-divisionpermutations which are assigned the given second sub-division value.

When assigning, based on said frequency analysis, a first (or second)respective sub-division value to each of said plurality of possiblefirst sub-division permutations, said assigning may comprise: grouping,based on said frequency analysis, said plurality of possible first (orsecond) sub-division permutations into a plurality of sets (or‘levels’). Each set may comprise at least one first (or second)sub-division permutation. The at least one first (or second)sub-division permutation in each set may have a corresponding occurrencelevel that falls within a different respective range of occurrencelevels associated with that set.

For a given first (or second) sub-division value, the number of firstsub-division permutations which are assigned the given firstsub-division value may depend on the set associated with the first (orsecond) sub-division permutation(s) which are assigned the given firstsub-division value.

Forming a processed sequence of bits may further comprise including aheader portion in the processed sequence, said header portion comprisingextraction information for use in reconstructing said input sequence ofbits from said processed sequence, and the extraction information beingconfigured for use in identifying the respective portion permutationwhich each label represents.

The extraction information may be configured for use in identifying howthe said plurality of possible first (or second) sub-divisionpermutations are grouped into sets. The extraction information mayidentify how many first (or second) sub-division permutations each setcomprises. The extraction information may be further configured toidentify the processing bit length used in processing said inputsequence of bits. The extraction information may be further configuredto identify how each portion is sub-divided into a plurality ofsub-divisions. The extraction information may be further configured toidentify how many bits each first sub-division comprises and how manybits each second sub-division comprises. The extraction information maybe further configured to identify how many bits the input sequence ofbits comprises.

The process may further comprise repeating steps (i) to (vii) at leastone further time using said processed sequence as said input sequence.

According to one aspect of the invention there is provided a method ofprocessing data, the method comprising the steps of: (i) dividing thedata into a plurality of processing segments wherein each processingsegment comprises an input sequence of bits; (ii) identifying a currentprocessing bit length for use in processing a current processing segmentof said data to form a processed segment meeting at least onepredetermined processing criterion; (ii) dividing the current processingsegment into a plurality of portions wherein each portion has arespective portion bit length equal to said current processing bitlength and wherein the bits in each portion are arranged in a respectiveone of a number of possible permutations; (iv) assigning a respectivelabel to each of a plurality of said possible permutations; and (v)forming a processed segment by replacing, within said current processingsegment, bit portions comprising bits arranged in one of said pluralityof possible permutations with the respective label assigned to that oneof said possible permutations; (vi) identifying a new processing bitlength for use in processing a next processing segment of said data toform a processed segment meeting at least one predetermined processingcriterion; (vii) repeating, for each of said plurality of processingsegments, steps (ii) to (vi) wherein the new processing bit length isused as the current processing bit length and the next processingsegment of said data is used as the current processing segment, andwherein a processing bit length used for at least one of said processingsegments of said data is different to a processing bit length used forat least one other of said processing segments of said data.

According to one aspect of the invention there is provided a method ofprocessing data comprising an input sequence of bits, the methodcomprising the steps of: (i) setting a current processing bit length, ofat least one bit, for use in processing said input sequence of bits;(ii) dividing the input sequence of bits into a plurality of portionswherein each portion has a respective portion bit length equal to saidcurrent processing bit length and wherein the bits in each portion arearranged in a respective one of a number of possible permutations; (iii)for each of a plurality of possible permutations analysing the inputsequence of bits to respectively identify how many times, within saidinput sequence of bits, a portion having that possible permutationoccurs; (iv) determining whether at least one predetermined processingcriterion has been achieved by comparing results of said analysing withthe predetermined processing criterion; (v) processing said inputsequence of bits based on said determining wherein said processingcomprises: when the determining determines that the predeterminedprocessing criterion has not been achieved performing at least one of:setting a new processing bit length that is different to the currentprocessing bit length and repeating steps (ii) to (v) using said newprocessing bit length as the current processing bit length; and endingprocessing of said input sequence of bits; and when the determiningdetermines that the at least one predetermined processing criterion hasbeen achieved: assigning a respective label to each of said plurality ofpossible permutations; and forming a processed sequence of bits byreplacing, within said sequence of bits, bit portions comprising bitsarranged in one of said plurality of possible permutations with therespective label assigned to that one of said possible permutations.

The predetermined processing criterion may comprise whether 50% of thepossible permutations which occur in the input sequence of bits occur atleast twice as frequently as the other 50% of the possible permutationswhich occur in the input sequence of bits.

The predetermined processing criterion may comprise whether 50% of thepossible permutations occur in the input sequence of bits.

According to one aspect of the invention there is provided a method ofreconstructing a processed sequence of bits produced by a methodaccording to any preceding claim, the method of reconstructing aprocessed sequence comprising the steps of: obtaining extractioninformation for use in reconstructing an original sequence of bits fromsaid processed sequence; reconstructing said original sequence of bitsfrom said processed sequence based on said extraction information.

According to another aspect there is provided a method of compression inwhich an input sequence of bits is divided into a plurality of portions;each portion is sub-divided into a plurality of sub-divisions; frequencyanalysis is performed to determine the number of occurrences of eachsub-division permutation and new values are assigned, based on thefrequency analysis, to each of the sub-division permutations. For eachportion a label, representing the permutation of bits in that portion,is assigned, wherein the label comprises a representation of a combinedvalue resulting from combining the new values associated with thesub-division permutations of that portion. A processed sequence of bitsis generated by replacing, within the input sequence of bits, bitportions with the respective label representing the permutation of bitsin that portion.

According to another aspect there is provided a method of processingdata comprising an input sequence of bits, the method comprising thesteps of: (i) identifying a processing bit length for use in processingsaid input sequence of bits; (ii) dividing the input sequence of bitsinto a plurality of portions wherein each portion has a respectiveportion bit length equal to said processing bit length and wherein thebits in each portion are arranged in a respective portion permutation;(iii) respectively sub-dividing each portion into a plurality ofsub-divisions comprising at least a first sub-division and a secondsub-division, wherein each sub-division of the plurality ofsub-divisions comprises at least one bit, wherein the at least one bitof each first sub-division is arranged in a respective firstsub-division permutation, and wherein the at least one bit of eachsecond sub-division is arranged in a respective second sub-divisionpermutation; (iv) performing frequency analysis: to determine, for eachof a plurality of possible first sub-division permutations, how manytimes, within said input sequence of bits, a portion comprises a firstsub-division having bits arranged in that possible first sub-divisionpermutation; and to determine, for each of a plurality of possiblesecond sub-division permutations, how many times, within said inputsequence of bits, a portion comprises a second sub-division having bitsarranged in that possible second sub-division permutation; (v) assigninga respective sub-division value to each of said plurality of possiblefirst sub-division permutations based on how many times, within saidinput sequence of bits, a portion comprises a first sub-division havingbits arranged in that possible first sub-division permutation; andassigning a respective sub-division value to each of said plurality ofpossible second sub-division permutations based on how many times,within said input sequence of bits, a portion comprises a secondsub-division having bits arranged in that possible second sub-divisionpermutation; (vi) for each portion permutation of a plurality ofpossible portion permutations, generating a respective labelrepresenting that portion permutation, wherein said generating comprisescombining: the sub-division value assigned to the first sub-divisionpermutation corresponding to the first sub-division of that portionpermutation; with the sub-division value assigned to the secondsub-division permutation corresponding to the second sub-division ofthat portion permutation; wherein said respective label comprises arepresentation of a combined value resulting from said combining; and(vii) forming a processed sequence of bits by replacing, within saidinput sequence of bits, bit portions comprising bits arranged in one ofsaid plurality of possible portion permutations, with the respectivelabel representing that one of said plurality of possible portionpermutations.

When generating, for each portion permutation, a respective labelrepresenting that portion permutation, said combining may comprise:arithmetically adding said sub-division value assigned to the firstsub-division permutation corresponding to the first sub-division of thatportion permutation to said sub-division value assigned to the secondsub-division permutation corresponding to the second sub-division ofthat portion permutation; wherein said combined value comprises a resultof said addition.

When generating, for each portion permutation, a respective labelrepresenting that portion permutation, said generating may furthercomprise: when a first particular sub-division value is assigned for aplurality of different first sub-division permutations: generating, foreach of said respective plurality of different first sub-divisionpermutations having that first particular sub-division value, adifferent respective first additional value for use in discriminatingbetween said respective plurality of first sub-division permutationshaving that first particular sub-division value; and when a secondparticular sub-division value is to be assigned for a plurality ofdifferent second sub-division permutations: generating, for each of saidrespective plurality of different second sub-division permutationshaving that second particular sub-division value, a different respectivesecond additional value for use in discriminating between saidrespective plurality of second sub-division permutations having thatsecond particular sub-division value.

For a first given sub-division value, the number of first sub-divisionpermutations which are assigned the first given sub-division value maydepend on the occurrence levels associated with the first sub-divisionpermutations which are assigned the first given sub-division value; andfor a second given sub-division value, the number of second sub-divisionpermutations which are assigned the second given sub-division value maydepend on the occurrence levels associated with the second sub-divisionpermutations which are assigned the second given sub-division value.

When assigning, based on said frequency analysis, a respectivesub-division value to each of said plurality of possible first (orsecond) sub-division permutations, said assigning may comprise:grouping, based on said frequency analysis, said plurality of possiblefirst (or second) sub-division permutations into a plurality of sets (or‘levels’); wherein each set comprises at least one first (or second)sub-division permutation; and wherein the at least one first (or second)sub-division permutation in each set has a corresponding occurrencelevel that falls within a different respective range of occurrencelevels associated with that set.

For a first (or second) given sub-division value, the number of firstsub-division permutations which are assigned the first (or second) givensub-division value may depend on the set associated with the first (orsecond) sub-division permutation(s) which are assigned the first (orsecond) given sub-division value.

The method may further comprise repeating steps (i) to (vii) at leastone further time using said processed sequence of bits as said inputsequence of bits.

The sub-division values assigned to each of the plurality of possiblefirst sub-division permutations and each of the plurality of secondsub-division permutations may be assigned such that sub-division valuesassigned to permutations with a lower occurrence level have higherlevels of statistical redundancy than the sub-division values assignedto permutations with a higher occurrence level.

According to another aspect there is provided a method of processingdata, the method comprising the steps of: (i) dividing the data into aplurality of processing segments wherein each processing segmentcomprises an input sequence of bits; (ii) identifying a currentprocessing configuration defining a current processing bit length foruse in processing a current processing segment of said data to form aprocessed segment meeting at least one predetermined processingcriterion; (ii) dividing the current processing segment into a pluralityof portions wherein each portion has a respective portion bit lengthequal to said current processing bit length and wherein the bits in eachportion are arranged in a respective one of a number of possiblepermutations; (iv) assigning a respective label to each of a pluralityof said possible permutations; (v) forming a processed segment byreplacing, within said current processing segment, bit portionscomprising bits arranged in one of said plurality of possiblepermutations with the respective label assigned to that one of saidpossible permutations; (vi) identifying a new processing configurationfor use in processing a next processing segment of said data to form aprocessed segment meeting at least one predetermined processingcriterion; and (vii) repeating, for each of said plurality of processingsegments, steps (ii) to (vi) wherein the new processing configuration isused as the current processing configuration and the next processingsegment of said data is used as the current processing segment, andwherein the processing configuration used for at least one of saidprocessing segments of said data defines a different processing bitlength to a processing bit length defined by a processing configurationused for at least one other of said processing segments of said data.

Each processing segment may be assigned a marker which representscharacteristics of the data within the processing segment, and thecurrent processing configuration may be identified based on the markerassigned to the current processing segment.

Each processing configuration may define one of: a plurality ofsub-divisions of each portion, each sub-division having a respectivesub-division bit length, wherein a sum of said respective sub-divisionbit lengths equals said processing bit length; and an undividedprocessing portion, the bit length of which is said processing bitlength.

The processing configuration used for at least one of said processingsegments of said data may define a first plurality of sub-divisionshaving a first combination of sub-division bit lengths; and theprocessing configuration used for at least one other of said processingsegments of said data may define a second plurality of sub-divisionshaving a second combination of sub-division bit lengths; and the firstcombination of sub-division bit lengths may be different to the secondcombination of sub-division bit lengths.

The processing configuration used for at least one of said processingsegments of said data may define a plurality of sub-divisions having acombination of sub-division bit lengths; and the processingconfiguration used for at least one other of said processing segments ofsaid data may define an undivided processing portion.

The method may further comprise, between steps (v) and (vi), identifyinga new processing configuration for use in reprocessing the processedsegment and repeating steps (ii) to (v) wherein the new processingconfiguration is used as the current processing configuration and theprocessed segment of said data is used as the current processingsegment.

According to another aspect there is provided a method of processingdata comprising an input sequence of bits, the method comprising thesteps of: (i) identifying a current processing configuration defining acurrent processing bit length for use in processing said input sequenceof bits, wherein the current processing configuration defines aplurality of sub-divisions of each portion, each sub-division having arespective sub-division bit length, wherein a sum of said respectivesub-division bit lengths equals said current processing bit length; (ii)dividing the input sequence of bits into a plurality of portions, eachportion comprising one or more sub-divisions according to the currentprocessing configuration, wherein each portion has a respective portionbit length equal to said current processing bit length and wherein thebits in each sub-division are arranged in a respective one of a numberof possible sub-division permutations; (iii) for each of a plurality ofpossible sub-division permutations, analysing the input sequence of bitsto respectively identify how many times, within said input sequence ofbits, a portion comprises a sub-division having that possiblesub-division permutation occurs; (iv) determining whether at least onepredetermined processing criterion has been achieved by comparingresults of said analysing with the predetermined processing criterion;(v) processing said input sequence of bits based on said determiningwherein said processing comprises: when the determining determines thatthe predetermined processing criterion has not been achieved, performingat least one of: identifying a new processing configuration that isdifferent to the current processing configuration and repeating steps(ii) to (v) using said new processing configuration as the currentprocessing configuration; and ending processing of said input sequenceof bits; and when the determining determines that the at least onepredetermined processing criterion has been achieved: assigning arespective sub-division value to each of said plurality of possiblesub-division permutations; and forming a processed sequence of bits byreplacing, within said sequence of bits, bit portions comprising asub-division having bits arranged in one of said plurality of possiblesub-division permutations with a portion label based on the sub-divisionvalues assigned to that sub-division permutation.

The respective sub-division value assigned to each of said plurality ofpossible permutations may be based on how many times, within said inputsequence of bits, a portion comprises a sub-division having bitsarranged in that possible permutation.

The sub-division values assigned to each of the plurality of possiblepermutations may be assigned such that sub-division values assigned topermutations which occur less often have higher levels of statisticalredundancy than the sub-division values assigned to permutations whichoccur more often.

When the determining determines that the predetermined processingcriterion has not been achieved and a new processing configuration isidentified, the new processing configuration may be selected in apredetermined order, for example ascending order of processing bitlength.

The input sequence of bits may comprise a processing segment, and theprocessing segment may be assigned a marker which represents adistribution characteristic of the data within the processing segment,and said identification of current processing configuration may be basedon the marker of the processing segment.

Identification of the current processing configuration may compriseusing the marker of the processing segment to identify a processingconfiguration which has previously been used to process a differentprocessing segment (e.g. in a different file).

The marker may be determined based on mathematical analysis of thedistribution characteristic of the data within the processing segment.

The marker may be determined by: dividing the input sequence of bitsinto a plurality of portions, where the bits in each portion arearranged in a respective one of a number of possible portionpermutations; determining the occurrence of each possible portionpermutation within the input sequence of bits; and measuring thedistribution of the occurrences of the possible portion permutations.

The distribution characteristic may comprise at least one of: theaverage byte value of the data within the processing segment, theaverage change in byte value of the data within the processing segment,and the average change in byte value occurrence of the data within theprocessing segment.

The marker may comprise a multi-dimensional marker.

The processing configuration may be one of a plurality of processingconfigurations, each having a respective reference number, and saidprocessing configuration may be identified by means of its referencenumber.

Each reference number may provide a binary representation of thesub-divisions defined by the corresponding processing configuration.

The processing configuration may be identified based on Fourier analysisof the input sequence of bits.

The processing configuration may be identified by performing Fourieranalysis on the input sequence of bits and obtaining at least oneFourier coefficient; selecting a processing bit length based on the atleast one Fourier coefficient; and identifying a processingconfiguration indicating the selected processing bit length.

The predetermined processing criterion may comprise whether at least onepossible permutations does not occur in the input sequence of bits.

The predetermined processing criterion may comprise whether a measure ofa distribution (e.g. a coefficient of variation) of occurrences of thepossible permutations within the sequence of bits exceeds a threshold.

According to another aspect there is provided a method of processingdata, the method comprising the steps of: (i) dividing the data into aplurality of processing segments wherein each processing segmentcomprises an input sequence of bits; (ii) performing a mathematicalanalysis of a processing segment to determine a distributioncharacteristic of data within the processing segment and assigning atleast one marker to the processing segment based on the mathematicalanalysis; (ii) identifying, based on the marker assigned to theprocessing segment, a current processing configuration defining acurrent processing bit length for use in processing a current processingsegment of said data to form a processed segment meeting at least onepredetermined processing criterion; (ii) dividing the current processingsegment into a plurality of portions wherein each portion has arespective portion bit length equal to said current processing bitlength and wherein the bits in each portion are arranged in a respectiveone of a number of possible permutations; (iv) assigning a respectivelabel to each of a plurality of said possible permutations; and (v)forming a processed segment by replacing, within said current processingsegment, bit portions comprising bits arranged in one of said pluralityof possible permutations with the respective label assigned to that oneof said possible permutations.

The current processing configuration may define a plurality ofsub-divisions of each portion, each sub-division having a respectivesub-division bit length, wherein a sum of said respective sub-divisionbit lengths equals said current processing bit length.

Aspects of the invention extend to computer program products such ascomputer readable storage media having instructions stored thereon whichare operable to program a programmable processor to carry out a methodas described in the aspects and possibilities set out above or recitedin the claims and/or to program a suitably adapted computer to providethe apparatus recited in any of the claims.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE INVENTION

Embodiments of the invention will now be described, by way of exampleonly, with reference to the attached figures in which:

FIG. 1a is a simplified schematic block diagram illustrating a systemfor compressing and decompressing data;

FIG. 1b is a flow chart illustrating an overview of a method ofcompression;

FIG. 1c is a flow chart illustrating an overview of a method ofdecompression;

FIG. 2 illustrates the main data groups used in the methods ofcompression described below, including exemplary data sizes/values forthe purposes of explanation only;

FIGS. 3A, 3B, 3C and 3D illustrate how a bit portion length is selectedin a first example;

FIGS. 4A and 4B illustrate how a bit portion length is selected in asecond example;

FIGS. 5A, 5 bB, 5C, 5D and 5E illustrate an alternative method ofselecting a bit portion length;

FIGS. 6A, 6B, 6C and 6D illustrate a method of determining whichconfiguration of combination arrays to use once a bit portion length hasbeen determined according to one or more of the methods of FIGS. 3A to3D, 4A and 4B and 5A to 5E;

FIGS. 7A and 7B illustrate a first part of a method of assigning labelsto bit portions once a combination array CA configuration has beenselected according to the method illustrated in FIGS. 6A to 6D;

FIGS. 8A, 8B, 8C and 8D are tables detailing possible combined new CAvalues with their corresponding new CA₀ values and new CA₁ values;

FIG. 9 is a table detailing possible combination of CA₀ disambiguationvalues and CA₁ disambiguation values, and the resulting combineddisambiguation values, for the example illustrated in FIGS. 7A and 7B;

FIG. 10 illustrates how labels are assigned to bit portions;

FIG. 11 is a table listing all of the possible bit portions of length 6bits and the labels assigned to each bit portion, based on thecombination arrays CA₀ and CA₁ in FIGS. 7A and 7B;

FIGS. 12A, 12B, 12C and 12D are examples of generating new CA values(and disambiguation values) for bit portions having a bit portion lengthof 8 bits, using a particular CA configuration;

FIGS. 13A, 13B, 13C and 13D are simplified representations of fourexemplary header structures;

FIG. 14 illustrates the target maximum BP and/or CA values calculated inaccordance with an alternative embodiment;

FIG. 15 illustrates a number of CA configurations and associatedreference numbers;

FIG. 16 illustrates a method of determining which configuration ofcombination arrays to use to divide up a processing segment;

FIG. 17 is a simplified representation of a further exemplary headerstructure;

FIGS. 18a and 18b are tables showing extracts from an exemplary 65536byte processing segment and data related to the processing segment;

FIGS. 19a, 19b, 19c and 19d are graphs plotting the data shown in thetables of FIGS. 18a and 18 b;

FIGS. 20a , 20B and 20 c are schematic diagrams illustrating asimplified overview of how a segment marker is generated;

FIGS. 21a, 21b, 21c, 21d, 21e, 21f, 21g and 21h illustrate the processof populating the three dimensional segmark matrix and populating anassociated table of successful CA configurations;

FIGS. 22a, 22b and 22c illustrate schematically steps of a method ofanalysing a processing segment using Fourier analysis to determine a bitlength L_(BP) to use in splitting up the processing segment into bitportions and/or combination arrays;

FIGS. 23a and 23b : 23 a is a table showing every possible 4 bit binaryvalue from 0000 to 1111, in which a recompression index is assigned toeach binary value, and 23 b is a table showing optimised binary valueswhich are assigned to combined new CA values;

FIGS. 24a and 24b are equivalent to FIGS. 23a and 23b , but instead showhow binary values with a bit length of 6 are optimised;

FIGS. 25a and 25b : 25 a is an extract from an exemplary array whichrepresents a segment of randomly organised and evenly distributed data,and 25 b is a table showing the number of occurrences, within thesegment, of the first 17 byte values;

FIG. 26 is an extract from the exemplary array of FIG. 25a written as abinary stream;

FIGS. 27a, 27b, 27c and 27d are extracts from the exemplary array ofFIG. 25a , written as a binary stream and split into portions havingdifferent bit lengths;

FIGS. 28a, 28b, 28c and 28d are tables showing the number ofoccurrences, within the segment, of a selection of portion values,including the portion values having the highest and lowest occurrences.

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

FIG. 1a is a simplified schematic block diagram illustrating a systemfor compressing and decompressing data, and FIGS. 1b and 1c show relatedmethods. The system of FIG. 1a comprises compression apparatus 105 forcompressing a file 201 to produce a compressed file 202.

The system of FIG. 1a also comprises decompression apparatus 505 fordecompressing a compressed file 202, which has been compressed using thecompression apparatus 105, in order to re-create the original file 201.

As indicated in FIG. 1a the file 201 may comprise, for example, a textdocument, music data, the contents of a database or video data.

The compression apparatus 105 is configured to extract data comprising asequence of bits from the file 201, the sequence of bits correspondingto a processing segment 203. The processing segments 203 can beconfigured, on the fly, to be any suitable size, therefore allowing theprocessing segment size to be selected adaptively based, for example, onthe processing capabilities of the compression apparatus 105 or otherrelevant factors.

The compression apparatus 105 comprises a bit portion module 253, whichis beneficially configured to analyse each of the processing segments203 and select, based on this analysis, a bit portion length L_(BP)(also referred to as a bit length) for use in dividing the processingsegments into smaller data units referred to as ‘bit portions’ 205. Asan example, FIG. 1a illustrates a processing segment having beenassigned a bit portion length L_(BP) of 8 bits by the bit portion module253, however the bit portion module 253 is configured to select arespective bit portion length L_(BP) based on frequency analysis of eachprocessing segment 203, and therefore different processing segments canbe assigned different bit portion lengths. Using this frequencyanalysis, the bit portion module 253 is configured to select the bitportion length L_(BP) based on which bit portion length L_(BP)apparently provides the best (or among the best) prospects forcompression. The bit portion module 253 can also be configured to selectany bit portion length L_(BP) with acceptable prospects for compression,for example to optimise for speed as opposed to compression.

If the bit portion module 253 determines that no bit portion length willallow compression of the processing segment 203 (or the compression doesnot meet a predefined compression threshold, for example a greater than5% reduction in size), it is configured to refrain from assigning a bitportion length to the processing segment, and the processing segment 203will be output by the compression apparatus 105 in its original(unprocessed) form.

Once a bit portion length is selected and the processing segment 203sub-divided into bit portions 205 accordingly, the bit portions 205 mayadvantageously be further sub-divided into smaller data sub-divisionsreferred to as combination arrays (although, depending on requirements,such further sub-division may not be implemented). These combinationarrays represent the smallest data unit used in processing theprocessing segment 203.

The way in which a file may be sub-divided into smaller data units toaid efficient processing is described in more detail below, in thesection titled ‘Overview—Main Data Groups’, with reference to FIG. 2.

The compression apparatus 105 further comprises a label assignmentmodule 255 which is configured to assign a respective label to eachpermutation of bits represented by the bit portions 205, based onanalysis of the frequency of occurrence of the bit portion valuecorresponding to that permutation, and/or frequency of occurrence ofcombination array values that form that permutation, within a processingsegment.

The way in which a label for a bit portion permutation may be assignedis introduced below in the section titled ‘Overview—Assigning Labels’.

Where a bit portion is sub-divided into combination arrays, therespective combination array values within each bit portion 205 areassigned a new value (or ‘label’). The new values assigned to thecombination array values within each bit portion are combined togetherand, if necessary, the resulting combination concatenated with anyadditional information required for transforming the resultingcombination back into its original form. The respective combination foreach bit portion 205, together with any information concatenated withthat combination, form a bit portion label that is, in effect, assignedto a corresponding permutation bits represented by that bit portion 205.In so doing, each bit portion label is, in effect, also assigned toevery bit portion 205 comprising bits arranged in the permutationassociated with that label.

The concept of combining different data values is introduced below, inthe section titled ‘Overview-Combine Method’. The way in whichcombination array values may be labelled and combined to form a labelfor a bit portion permutation is described in more detail below in thesection titled ‘Method of Assigning labels to Bit Portion Permutationsusing Combination Arrays’.

The label assignment module 255 is configured to output a processedsegment 209 corresponding to a processing segment 203 in which each bitportion 205 has been replaced with the bit portion label assigned to thepermutation of bits represented by that bit portion 205. In thisexample, the resulting processed segment 209 is smaller than theprocessing segment and can thus be thought of as a ‘compressed’ segment.The processed segment 209 comprises each of the labels assigned to thebit portions 205 of the processing segment 203.

The compression apparatus 105 further comprises a header generationmodule 257 which is configured to generate a header 211 for eachprocessing segment 203. The header 211 comprises extraction informationwhich is used by the decompression apparatus 505 to extract theprocessing segment 203 from the processed segment 209. The extractioninformation preferably allows the decompression apparatus 505 tointerpret the labels in the processed segment 209 in order to allow thedecompression apparatus 505 to map the labels to their associated bitportion values.

Preferably, each header starts with a compression method signature, andprovides information relating to the chosen bit portion length L_(BP),the combination array configuration used, the size of the originalprocessing segment 203, and information on how labels were assigned toeach of the bit portions 205.

As visually indicated in FIG. 1a , the total size of each of theprocessed segments 209 in combination with its header 211 is less thanthe size of the corresponding processing segment 203. Furthermore, thesize of the processed segments 209 and their associated headers 211 mayvary.

As shown in FIG. 1a , the compression apparatus 105 outputs a compressedfile 202, which comprises fewer bits in total than the original file201. This is due to the fact that the size of each of the processedsegments 209 in combination with its header 211 is less than the size ofthe corresponding processing segment 203.

The decompression apparatus 505 is configured to process each header 211and associated processed segment 209 of the compressed file 202. Eachheader can be identified, for example, by the signature included in theheader.

The decompression apparatus 505 comprises a header decoding module 557and a label decoding module 555. The header decoding module 557 isconfigured to decode the information in the header 211, for use by thelabel decoding module 555 in decoding the labels in the processedsegment 209 and thus map the labels to their associated bit portionvalues. The label decoding module is configured to output a processingsegment 203 comprising all the bit portion values associated with thelabels in the processed segment 209. The processing segment 203therefore corresponds to the original processing segment 203.

The system for compressing and decompressing data illustrated in FIG. 1acan alternatively or additionally be used to encrypt and decrypt data.Any file 202 produced by the apparatus 105 will exhibit some level ofencryption, because the information contained in the file 202 isrepresented by different data to that used in the original file 201. Insuch embodiments where the system of FIG. 1a is used to encrypt and/ordecrypt data, the total size of each of the processed segments 209 incombination with its header 211 may be greater than the size of thecorresponding processing segment 203. Accordingly, when the apparatus105 is used as encryption/decryption apparatus, the encrypted file 202output by the encryption side of the apparatus 105 may not always be a‘compressed’ file.

FIG. 1b is a flow chart illustrating, in overview, a method ofcompression that may be employed by the compression apparatus 105 ofFIG. 1 a.

In the method of FIG. 1a , at step 111 an input sequence of bits isdivided into the processing segments. At step 113 the determination ismade of whether there is a bit portion length that will allowcompression of the processing segment 203 (or the compression does notmeet a predefined compression threshold, for example a greater than 5%reduction in size). In other words it is determined whether thepotential compression level for the current processing segment isacceptable, for example whether a predetermined processing criterion issatisfied.

If it is determined that the potential compression level for the currentprocessing segment is acceptable, the method continues to step 115 inwhich the current segment is processed, as described above.Specifically, the possessing segment is analysed and a bit length isselected based on the analysis. The labels are then assigned to each ofthe bit portions. Extraction information for use in reconstructing theoriginal processing segment is then generated and, in this example,placed in a header. More detail on how the current segment is processedis provided in FIGS. 2-10 and the associated description.

At step 117, it is determined whether to attempt to reprocess thecurrent segment. If the current segment is to be reprocessed, theprocessed segment (including the header if present) is used as thecurrent segment, and the method returns to step 113. If the currentsegment is not reprocessed, the method continues to step 119 where aprocessed segment is output.

If at step 113 it is determined that the potential compression level forthe current processing segment is not acceptable, the method continuesto step 125 in which the current segment is used as the processedsegment, without any processing (or further processing) of the currentsegment. Then, at step 119, the processed segment is output.

After the processed segment is output, it is determined at step 121whether there is another processing segment in the input sequence ofbits for processing. If yes, the next processing segment of the inputsequence of bits is used as the current segment, and the method returnsto step 113.

If it is determined at step 121 that there are no more processingsegments in the input sequence of bits for processing, the processedsegments are output together as a processed file at step 123.

FIG. 1c is a flow chart illustrating in overview, a method ofdecompression that may be employed by the decompression apparatus 505 ofFIG. 1 a.

At step 131 the first processed segment of processed file is used as thecurrent segment.

At step 133 it is determined whether extraction information is availablefor the current segment. In this example, any extraction information isfound in the header of the processed segment. If extraction informationis available, the method proceeds to step 135 where extractioninformation is obtained for current segment, for example from anassociated header.

Next, at step 137, the processing segment in its form prior toprocessing is reconstructed from the current segment, based onextraction information.

At step 147, the reconstructed segment is used as the current segment,and the method returns to step 133.

If, at step 133, extraction information is not available, the methodproceeds to step 145 where the current segment is used as thereconstructed segment, without any reconstruction (or furtherreconstruction) of the current segment. Then, at step 139, the processedsegment is output.

Next, at step 141, it is determined whether there is another processedsegment of the processed file. If yes, the next processed segment of theprocessed file is used as the current segment, and the method returns tostep 133.

If it is determined at step 141 that there are no more processedsegments in the processed file, the reconstructed segments are outputtogether as a reconstructed file at step 143.

It will be appreciated that the methods of compressing and decompressingdata described herein can beneficially be used in various applications.

For example, compressing data using the methods described herein canallow larger amounts of data to be stored in a given storage medium, andlarger amounts of data to be transmitted in any given transmission ofdata. This in turn will reduce the cost for data storage, which could beparticularly advantageous where large amounts of data need to be stored,such as in data farms. Cost saving can be made because, for example,data farms will require less power to maintain their data storingdevices. Advantageously, even if different types of data are beingstored (e.g. in a data farm) the methods of compression described allowcompression to be achieved for generally any data, regardless of thedata type (e.g. audio, text, video).

In the field of telecommunications, the described techniques can be usedto compress data before transmission, which would allow a reduction inthe amount of resources needed to make transmissions.

Devices can be configured to carry out both compression anddecompression of data according to the described methods, or devices canbe configured to carry out only one of compression and decompression.Media-playing devices, such as mobile phones and DVD players, may onlybe configured to decompress compressed media files using the methodsdescribed herein. In some cases such media-playing devices may beprovided with a dedicated chip for this purpose, or the decompressionmay be performed by software modules in the device which are not tied toany specific hardware. Providing the processing power of a device issufficient and enough storage space is available, entire files can bedecompressed before use (for example a short video clip can bedecompressed and then viewed). In other cases, files can be decompressedon the fly during use (for example a film can be decompressed andwatched simultaneously). Considering, mobile phones, storing data incompressed form and then decompressing the data when required using themethods described herein would allow significant amounts of space to besaved on mobile phones, for example allowing multiple high quality filmsto be stored on the mobile phone memory.

Although the time and/or power taken to compress/decompress a givenpiece of data can vary, in many instances compression takessignificantly longer (and/or requires more processing power) thandecompression. In some applications this is not especially limiting, forexample where films are compressed at a central internet server, anddownloaded or streamed in compressed form and then decompressed at auser device for viewing.

In some cases the time and/or processing power required for compressionusing the methods described herein can be greater than existingcompressions techniques. However, the methods described herein have theadvantage that greater compression can be achieved, and additionally oralternatively substantial compression can be achieved more consistentlyacross different types of data when compared to existing datacompression techniques. The compression methods described can achievethis because the ability to use different bit lengths and differentcombination array configuration when processing data means that, ineffect, different compression algorithms are applied, not only todifferent iterations of compression for the same file, but also todifferent parts of the same file.

As described below, the use of combination arrays allows header sizes tobe reduced. This is advantageous because headers 211 are generally addedto all compressed segments 209. This contrasts with many existingcompression techniques in which files are analysed as a whole, and datafor use in decompression, such as a hash table, relates to the file as awhole and is only included once in the compressed file.

The compression methods described herein advantageously analyse eachprocessing segment 203 of a file 201 individually, unlike existingcompression methods which analyse a file as a whole. Analysing theprocessing segments 203 individually (and analysing a processing segmentin multiple different ways using bit portions and/or combination arrays)allows the described methods to achieve better and more consistentcompression of data.

Overview—Main Data Groups

The way in which a file may be sub-divided into smaller data units toaid efficient processing will now be described, by way of example onlywith reference to FIG. 2.

FIG. 2 illustrates the main data groups used in the methods ofcompression described below, including exemplary data sizes/values forthe purposes of explanation only.

A file 201 may comprise, for example, a text document, a music file, adatabase or a video file. The file 201 may have any size; in thisexample the file size is 2 GB. As a further example, an ultra-high 4Kdefinition DVD is approximately 100 GB. A traditional high definitionDVD is approximately 6 GB. An hour of high definition downloadable videofrom the internet is approximately 1 GB. As an example, using thecompression techniques described below, it has been found that any ofthese types of file can be compressed, typically down to 1/64 of theiroriginal size.

In the compression methods described below, the file 201 is divided upinto one or more processing segments 203, which are generally smaller insize than the file 201. In this example, the 2 GB file 201 is broken upinto a plurality of 64 KB processing segments 203. Padding bits/bytesmay be used to ensure a file 201 can be divided into an integer numberof segments 203.

The processing segments 203 can be used where the size of the file 201is too large for a computer processor to read and/or process the wholefile at once. Generally most files fall into this category, however insome cases a whole file 201 may be read and/or processed without beingdivided into processing segments.

The size of the processing segments 203 is usually fixed and selectedbased on normal computer processing capabilities; however in someexamples the size of processing segments 203 is not fixed (seeModifications and Alternatives section).

The method involves assigning labels to groups of bits in a processingsegment 203, where the grouping of bits and corresponding labels arechosen in a way which ensures that the number of bits required torepresent the information of the processing segment 203 is less than theoriginal size of the processing segment 203 in bits. In overview,smaller labels (i.e. labels comprising fewer bits) are used to representmore frequently occurring groups of bits, while larger labels (i.e.labels comprising more bits) are used to represent less frequentlyoccurring groups of bits.

In preferred embodiments, two or more main groupings of bits in theprocessing segment are used: bit portions 205, and combination arrays207.

As illustrated in FIG. 2, each bit portion 205 generally comprises aplurality of consecutive bits, and each combination array 207 generallycomprises a sub-group of consecutive bits (or a single bit) from a bitportion 205.

In this example, a 64 KB processing segment 203 is divided into aplurality of bit portions 205 each having a bit portion length L_(BP) of6 bits. As shown in FIG. 2, each of the bit portions 205 comprises apermutation of 6 bits, where the first three bit portions havepermutations of 011100, 100110 and 111100 respectively. The first bitportion, comprising the bit permutation 011100, is considered to have abit portion (BP) value of 011100, or 28 in base 10.

Dividing each processing segment 203 up into bit portions 205 provides away of analysing the characteristics of the processing segment 203,where the results of this analysis are used to determine the prospectsfor compressing the segment 203 using a particular bit length.

Advantageously, the size of the bit portions 205 is not predetermined,and it can therefore be determined for each processing segment 203 whatsize of bit portion provides the best prospects for compressing thesegment 203.

In this example, the bit portion 205 has a bit portion length L_(BP) of6 bits, which are sub-divided into three combination arrays 207. Thefirst two combination arrays each comprise a single bit, and the thirdcombination array comprises four consecutive bits. As shown in FIG. 2,all bit portions 205 are divided up into combination arrays of the sameconfiguration—in this example the configuration is: [1 bit array][1 bitarray][4 bit array]. As also shown in FIG. 2, while the configuration(or pattern) of combination arrays 207 is the same for each bit portion205 of a processing segment 203, the contents of the combination arrays207 may vary between each bit portion 205, depending on the permutationof bits in each bit portion 205.

As shown in FIG. 2, each of the combination arrays comprises permutationof any number of bits (including one bit), where the number of bits inthe permutation depends on the combination array (CA) configuration. InFIG. 2, the first three combination arrays have permutations of 0, 1 and1100 respectively. These first three combination array permutations areconsidered to have combination array (CA) values of 0, 1 and 1100respectively; or 0, 1 and 12 respectively in base 10.

In some alternative embodiments, processing segments are only divided upinto groups of consecutive bits (or single bits) once, without thesegroups (e.g. bit portions 205) being sub-divided into further groups ofconsecutive bits or single bits (e.g. combination arrays 207).

Although in this example the bit portion 205 comprises three combinationarrays 207, the bit portion can advantageously be divided into anynumber of combination arrays 207, each combination array 207 having anysize. This means that the particular configuration of compression arrayscan be selected to provide optimised compression for a particularsegment. In this example, where the bit portion length L_(BP) of the bitportions 205 is 6 bits, there are 32 different possible configurationsof the combination arrays 207, as set out below:

TABLE 2 Configurations for a Combination Array Bit portion length L_(BP)of 6 {1, 1, 1, 1, 1, 1}, {1, 1, 1, 1, 2, 0}, {1, 1, 1, 2, 1, 0}, {1, 1,1, 3, 0, 0}, {1, 1, 2, 1, 1, 0}, {1, 1, 2, 2, 0, 0}, {1, 1, 3, 1, 0, 0},{1, 1, 4, 0, 0, 0}, {1, 2, 1, 1, 1, 0}, {1, 2, 1, 2, 0, 0}, {1, 2, 2, 1,0, 0}, {1, 2, 3, 0, 0, 0}, {1, 3, 1, 1, 0, 0}, {1, 3, 2, 0, 0, 0}, {1,4, 1, 0, 0, 0}, {1, 5, 0, 0, 0, 0}, {2, 1, 1, 1, 1, 0}, {2, 1, 1, 2, 0,0}, {2, 1, 2, 1, 0, 0}, {2, 1, 3, 0, 0, 0}, {2, 2, 1, 1, 0, 0}, {2, 2,2, 0, 0, 0}, {2, 3, 1, 0, 0, 0}, {2, 4, 0, 0, 0, 0}, {3, 1, 1, 1, 0, 0},{3, 1, 2, 0, 0, 0}, {3, 2, 1, 0, 0, 0}, {3, 3, 0, 0, 0, 0}, {4, 1, 1, 0,0, 0}, {4, 2, 0, 0, 0, 0}, {5, 1, 0, 0, 0, 0}, {6, 0, 0, 0, 0, 0},

In Table 2, each set of six numbers within curly brackets represents apossible configuration of combination arrays 207. Each number representsthe size of a combination array in bits, where 0 indicates that no arrayis used. For example, {1, 1, 3, 1, 0, 0} denotes dividing a bit portion205 into four combination arrays 207, the first two combination arrayscomprising a single bit each, followed by a 3 bit combination array, inturn followed by another single bit array.

It is noted that the total number of different possible configurationsof combination arrays depends on the bit portion length, where thenumber of possible configurations is equal to 2^(L) ^(BP) ⁻¹.

As stated above, the configuration of combination arrays is selected toprovide the best compression of a segment 203. Generally, all bitportions 205 of a particular processing segment 203 are divided into thesame configuration of combination arrays and the combination arrayconfiguration exploits any patterns, repetition and/or redundancy in theprocessing segment 203 in order to achieve effective compression.

Overview—Combine Method

The concept of combining different data values will now be introducedand explained, by way of example only.

A byte can hold a value between 0 (00000000) and 255 (11111111). TheASCII standard provides for representation of characters, letters orsymbols where each character, letter or symbol is represented using anASCII code which has a value of between 0 and 255. As a result, eachletter, character or symbol requires one byte of information to berepresented, as Table 1, below, illustrates.

TABLE 1 ASCII ASCII Letter Code Binary Letter Code Binary a 097 01100001A 065 01000001 b 098 01100010 B 066 01000010 c 099 01100011 C 06701000011 d 100 01100100 D 068 01000100 e 101 01100101 E 069 01000101 f102 01100110 F 070 01000110 g 103 01100111 G 071 01000111 h 104 01101000H 072 01001000 i 105 01101001 I 073 01001001 j 106 01101010 J 07401001010 k 107 01101011 K 075 01001011 l 108 01101100 L 076 01001100 m109 01101101 M 077 01001101 n 110 01101110 N 078 01001110 o 111 01101111O 079 01001111 p 112 01110000 P 080 01010000 q 113 01110001 Q 08101010001 r 114 01110010 R 082 01010010 s 115 01110011 S 083 01010011 t116 01110100 T 084 01010100 u 117 01110101 U 085 01010101 v 118 01110110V 086 01010110 w 119 01110111 W 087 01010111 x 120 01111000 X 08801011000 y 121 01111001 Y 089 01011001 z 122 01111010 Z 090 01011010

Considering, for example, the letters J and o, these have ASCII codes of74 (01001010) and 111 (01101111) respectively. Therefore, a conventionalrepresentation of the name Jo would be 0100101001101111, which is 16bits long.

The number of bits required to represent the name can be decreased bycombining the respective ASCII values using at least one mathematicaloperation. For example, the two values can be added together:74+111=185

Advantageously, the number 185 can be represented in binary using only 8bits (10111001), therefore saving 8 bits on the 16 bit value of0100101001101111.

However, the letters J and o are not the only combination of letterswhich would sum to give the total 185. For example, the letters I and pwould also yield the total 185 when added together. This is referred toas a collision.

Therefore, in this example it is necessary to provide additionaldisambiguation information in order to indicate which of the potentialcombinations of ASCII characters is being represented.

The number of collisions (i.e. combinations resulting in the same totalwhen combined using a mathematical operation such as addition) can bedecreased by changing the numeric value used to represent the charactersbeing combined.

For example, the first ASCII character value can be multiplied by 10before the two values are combined. Taking the example of “Jo” again:74×10+111=740+111=851

The number 851 can be represented in binary using only 10 bits(1101010011), therefore saving 6 bits on the 16 bit value of0100101001101111.

In this example, it is also necessary to provide additionaldisambiguation information in order to indicate which of the potentialcombinations of ASCII characters is being represented.

However, multiplying the first ASCII character value by 10 before thetwo values are added has the effect of reducing the number ofcombinations yielding the same result (“collisions”). This means thatless additional disambiguation information is required.

Collisions when combining bytes can be reduced still further byreplacing the ASCII values used to represent characters with numericlabels. Labels can also reduce the number of bits used to represent thecombined value. For example, if the letters J and O are represented bythe labels 0 and 1 respectively, then combining the two labels usingaddition results in a combined value of 1. As long as no othercharacters are assigned the labels 0 or 1, the combined value of 1 willbe unique, with no collisions occurring. Moreover, in this example thecombined value can be represented using only 1 bit.

Although described with reference to ASCII characters for ease ofunderstanding, the above-described methods of combining data can beapplied to any data, comprising any number of bits.

The methods described herein allow data, such as a file, to becompressed by dividing the data into groups of bits, assigning labels tothe groups of bits and then “combining” two or more of these groups ofdata together by combining their respective labels. In some embodiments,the combining comprises a mathematical operation such as addition.

In an e-book that uses letters and numbers (see Table 1), it is possiblethat either the first bit or the last bit is only ever 0 and the 1 isnever used, or vice versa, depending on encoding.

Advantageously, in preferred embodiments the way in which a file isdivided into groups of bits can be chosen in order to provide improvedcompression of the file. For example, when one part of the file is beingprocessed it may be divided up in a different way to another part of thefile.

Also, the preferred embodiments allow data from different types ofmedia, and by extension having vary different characteristics, to becompressed effectively, due to the flexibility when dividing the datainto groups of bits and assigning labels to the groups of bits. Existingcompression techniques tend to be more effective in compressingparticular types of media data (e.g. text, image data or the like)because they are better optimised for the inherent characteristics ofthat data. Advantageously, the preferred embodiments can achievecompression of files and/or data which would ordinarily be difficult tocompress using such existing compression techniques.

Overview—Assigning Labels

The bits of the processing segment are analysed to determine a way ofdividing the processing segment into groups of bits which will allowcompression to be achieved when labels are assigned to the groups ofbits. The processing segment is then divided into groups of bitsaccording to the determined configuration. The groups of bits maycomprise bit portions and/or combination arrays as introduced above.

Next, a label is assigned to each of the groups of bits, wherein eachlabel is unique (although generally only unique for the processingsegment being processed; labels may be reused between processingsegments). Some or all of the labels may comprise multiple parts.Preferably, all labels comprise a first part which acts as a primaryidentifier of the bit portion value (later referred to as “Combined newCA value”).

The first part of the bit label may uniquely (i.e. unambiguously)identify a bit portion value, in which case the label need only comprisethe first part. However, when the first part of the label does notunambiguously identify the bit portion value (i.e. multiple differentbit portion values are associated with the same first part of thelabel), the label further comprises a second part (later referred to as“Combined disambiguation information DI”).

The purpose of the second part of the label is to identify which of themultiple different bit portion values associated with the first part isbeing represented by the label.

In order to illustrate this with an example, consider the following fourdifferent bit portion values: 01011, 10110, 10111, 10010

Each of these four different bit portion values may be associated withthe same first part of a label (e.g. 11):

In such a case, each bit portion value can be unambiguously identifiedusing one of four second parts of the label (e.g. 00, 01, 10, 11):

In the examples provided here, the complete label for the bit portionvalues would be as follows:

Preferably, the length of the first part in bits remains constant forall bit portion values in a processing segment 203, while the length ofthe second part can vary, or the second part may not be used at all toidentify some bit portion values.

It can therefore be seen that the label as a whole can vary in length ofbits. All the labels used for the bit portion values of a particularprocessing segment can vary in length but share a common minimum length,corresponding to the length of the first part of the label. However,between different processing segments the length of the first part ofthe label can vary, as it is assigned based on frequency analysis of theprocessing segment (as described in further detail below).

Method of Selecting Bit Portion Length

FIGS. 3A to 3D illustrate a method of selecting what bit portion lengthL_(BP) should be used when dividing a processing segment 203 into anumber of bit portions 205.

This is done by dividing the processing segment 203 up into bit portions205 of different bit portion lengths L_(BP), and performing frequencyanalysis for each of the different bit portion lengths used.

Some existing compression techniques use fixed bit portion lengths. Ithas been found that by using variable bit portion lengths, which canchange depending on which part of a file is being processed, additionalcompression can be obtained which would otherwise not have beenachievable.

FIGS. 3A and 4A provide overviews of the frequency analysis resultsobtained for bit portion lengths 2 to 4 and 3 to 7 respectively, withdifferent exemplary results. FIGS. 3B, 3C, 3D and 4B illustratefrequency analysis performed on bit portion lengths of 2, 3, 4 and 7respectively.

As shown in FIG. 3A, in this example a bit portion length of 2 bits istested first. The processing segment 203 is divided up into a pluralityof bit portions 205, each having a bit portion length of 2 bits. Asshown in FIG. 3A, frequency analysis is performed on the bit portions205 of this initial bit portion length L_(BP)=2, and it is determinedwhether at least one of two criteria are fulfilled.

The first criterion is whether two or more compression “levels” (levelsare described further below) are present within the analysed bit portionBP values, and the second criterion is whether 50% or fewer of thepossible bit values are present in the processing segment 203.

If neither of the criteria are fulfilled, the bit portion length isincremented by one bit—to 3 bits—and the processing segment 203 isre-divided up into a plurality of bit portions 205, this time eachhaving a bit portion length of 3 bits. For each bit portion length beingtested, if the frequency analysis results fail to fulfil either of thetwo criteria, the next bit portion length is tested (i.e. the bitportion length is incremented by one bit and the processing segment 203is re-divided up into a plurality of bit portions 205, each having thesame number of bits as the current bit portion length).

FIG. 3B illustrates the frequency analysis performed on the plurality ofbit portions 205, in this case each having a bit portion length of 2bits. As each bit portion 205 of the processing segment 203 is only madeup of 2 bits, a bit portion 205 can only have one of four values—00, 01,10 or 11. Once the processing segment 203 has been divided into theplurality of bit portions 205, the number of occurrences of eachpossible bit portion value is determined (i.e. the frequency of eachvalue).

The bit portion values are then sorted in order of most occurring toleast occurring, as shown in FIG. 3B. In this example, the bit portionvalue 01 occurs the greatest number of times, with 65,538 occurrencesand the bit portion value 00 occurs the least number of times, with65,533 occurrences.

The number of compression levels is then determined based on the numberof occurrences of each of the bit portions values.

The level in which a bit portion (BP) value is placed determines howmany bits the label assigned to the BP value will have. All BP values inthe same level will be assigned the same number of bits. In preferredembodiments, the 1st level (level 0) is allocated labels with theminimum possible number of bits. Furthermore, in preferred embodiments,the labels allocated to each successive level are one bit longer thanthe previous level. An exemplary set of labels and associated labels areshown in Table 3 below.

TABLE 3 Level Label 0 00 0 01 1 100 1 101 2 1100 2 1101 2 1110 2 1111

In preferred embodiments, a “level” is defined as being a group of bitportion values in which none of the bit portion values occur less thanhalf as frequently as the most occurring bit portion value in thatgroup. For example, in a group of bit portion values where the mostoccurring bit portion value occurs 28,000 times, all of the bit portionvalues in the group will have occurrences greater than 14,000. In theexample shown in FIG. 3B, the least occurring bit portion value occurs65,533 times, and therefore all of the bit portion values are consideredto occupy the same level. Bit portion length L_(BP)=2 therefore fails tosatisfy the first criterion.

Next, it is determined whether 50% of the possible bit portion valuesoccur in the processing segment. For example, if only the bit portionvalues 01 and 11 occurred in the processing segment 203, and bit portionvalues 10 and 00 both never occurred, then exactly 50% of the possiblebit portion values are present in the processing segment. This would bean indication that the processing segment 203 can be compressed usingthe selected bit portion length. However, in the example illustrated inFIG. 3B all four of the possible bit portion values are present in theprocessing segment and therefore 100% of the possible bit portion valuesare present. As can be seen in FIG. 3A, the bit portion length of 2 bitsis listed as having one compression level and as not satisfying therequirement that 50% or fewer of the possible bit portion values arepresent. Bit portion length L_(BP)=2 therefore fails to satisfy thesecond criterion.

Therefore, the processing segment 203 is divided into a plurality of bitportions each having a bit portion length of 3 bits instead of 2 bitsand frequency analysis is again performed. This is illustrated in FIG.3C. FIG. 3C shows that if bit portion length L_(BP)=3 there are 8possible bit portion values.

The bit portion values are then sorted in order of most occurring toleast occurring, as shown in FIG. 3C. In this example, the bit portionvalue 011 occurs the greatest number of times, with 21,851 occurrencesand the bit portion value 101 occurs the least number of times, with21,833 occurrences.

Bit portion length L_(BP)=3 therefore fails to satisfy the firstcriterion.

Furthermore, in the example illustrated in FIG. 3C all eight (i.e. 100%)of the possible bit portion values are present in the processingsegment. Bit portion length L_(BP)=2 therefore fails to satisfy thesecond criterion.

Next, the processing segment 203 will be divided into a plurality of bitportions 205 having a bit portion length of 4 bits. This is illustratedin FIG. 3D.

FIG. 3D shows that if bit portion length L_(BP)=4 there are 16 possiblebit portion values.

As shown in FIG. 3D, bit portion values are sorted in order of mostoccurring to least occurring. In this example, the bit portion value0001 occurs the greatest number of times, with 27,369 occurrences andthe bit portion value 1110 occurs the least number of times, with 1,962occurrences.

Therefore, unlike for bit portion lengths 2 and 3 described above,multiple compression levels are present within the analysed bit portionBP values. Specifically, the 4th BP value (1001) occurs 12,646 times,which is less than half of 27,369. Therefore, the 4th bit portion valuebelongs to a 2^(nd) level (level 1).

Furthermore, the 8th BP value (1000) occurs 4,146 times, which is lessthan half of 12,646. Therefore, the 4th bit portion value belongs to a3^(rd) level (level 2).

This means that three levels are present, and bit portion lengthL_(BP)=4 therefore satisfies the first criterion.

As a result, bit portion length L_(BP)=4 would be selected as the chosenbit portion length in this example.

In the exemplary method of FIG. 4A, the processing segment 203 isinitially divided up into a plurality of bit portions 205 each having abit portion length of 3 bits (rather than 2 bits as illustrated in FIG.3A). As the exemplary results of FIG. 4A, none of bit portion lengths 3to 6 satisfy either of the criteria.

FIG. 4B shows exemplary frequency analysis results for bit portionlength L_(BP)=7. If bit portion length L_(BP)=7, there are 128 possiblebit portion values (some are omitted for legibility).

As shown in FIG. 4B, bit portion values are sorted in order of mostoccurring to least occurring. In this example, all bit portion valuesfrom the 10th value onwards have an occurrence of 0, and therefore bitportion length L_(BP)=7 satisfies the second criterion. BP values withan occurrence of 0 are not assigned to a level, and therefore the totalnumber of levels present for L_(BP)=7 is 1 (the first criterion istherefore not fulfilled).

As a result, bit portion length L_(BP)=7 would be selected as the chosenbit portion length in this example.

It is noted that in the particular example illustrated in FIG. 4B, it ispossible to achieve improved compression by assigning levels accordingto alternative embodiments, such as those described below.

Alternative Method of Selecting Bit Portion Length

FIGS. 5A to 5E illustrate an advantageous alternative method ofselecting a bit portion length L_(BP). The method involves testingmultiple bit portion lengths and determining if compression of theprocessing segment can be achieved using the bit portion length beingtested, and if so how much compression can be achieved.

The determination is made by assigning labels to each of the possiblebit portion (BP) values, and then determining whether the processingsegment 203 can be represented using fewer bits if the bit portions arerepresented using their respective labels (i.e. determining whether theprocessing segment 203 can be compressed using the labels). In order toassign the labels and make the determination as to whether compressioncan be achieved, frequency analysis is performed on the bit portionvalues to determine how many times each possible bit portion valueoccurs within the processing segment 203.

The frequency analysis results in a value for the achievable compressionof the processing segment 203 for each bit portion length tested (i.e.the minimum compression that is known to be achievable for theprocessing segment based on the chosen bit portion length). In FIG. 5A,bit portion lengths from 2 bits to 8 bits are tested, with achievablecompressions ranging from 3% (2 bits) to 25% (6 bits). It is noted thatthe final compression achieved for the selected bit portion length, oncethe full compression method described below has been carried out, may behigher than the achievable compression value.

It can also been seen from FIG. 5A that the bit portion length havingthe highest potential compression is 6 bits, whereas a bit portionlength of 8 bits would, for this particular segment being processed,have a lower potential compression. Therefore, in this case anycompression techniques which divide the processing segment into bytes(i.e. 8 bits) would fail to exploit potential additional compression.

As illustrated by the exemplary bit values in FIG. 5A, the sameprocessing segment 203 comprising the same bits may be analysed multipletimes, being divided into bit portions 205 of different sizes each time.

As shown in FIG. 5A, frequency analysis using different bit portionlengths is performed on a processing segment, in this example of size 64KB (only the first 16 bits and the final bit of the segment are shownfor simplicity).

FIGS. 5B and 5C illustrate the frequency analysis performed on theprocessing segment 203 when divided up into a plurality of bit portions205, each having a bit portion length of 4 bits. As each bit portion 205of the processing segment 203 is made up of 4 bits, a bit portion 205can have one of sixteen values—from 0 (0000) to 15 (1111).

Once the processing segment 203 has been divided into the plurality ofbit portions 205, the number of occurrences of each possible bit portionvalue is determined (i.e. the frequency of each value). The bit portionvalues are then sorted in order of most occurring to least occurring, asshown in FIG. 5B. In this example, the bit portion value 0001 occurs thegreatest number of times, and the bit portion value 1110 occurs theleast number of times.

The default order of bit portion values is from smallest to largest, andtherefore when two bit portion values have the same number ofoccurrences within a processing segment (which may be, for example,zero), the bit values are not sorted and accordingly will remain in sizeorder. As shown in FIG. 5B, each of the sorted bit portion values isassociated with a ranking corresponding to their sorted position. As canbe seen, the most occurring bit portion value is ranked 0 and the leastoccurring bit portion value is ranked 15.

In some embodiments, the sorted bit portion values are assigned newvalues which correspond to their ranking, with value 0000 correspondingto ranking 0, and value 1111 corresponding to value 15.

Referring to FIGS. 5B and 5C, in some embodiments the sorted bit portionvalues are not renumbered with new values, for example when fewer than50% of the BP values occur in the processing segment being analysed.

The occurrences of the bit portions are then analysed in order to splitthe BP values into levels where possible. As explained above, a “level”is defined as being a group of bit portion values in which none of thebit portion values occur less than half as frequently as the mostoccurring bit portion value in that group. For example, in a group ofbit portion values where the most occurring bit portion value occurs28,000 times, all of the bit portion values in the group will haveoccurrences greater than 14,000.

In the example shown in FIG. 5B, it is determined that the BP values canbe grouped to create three levels. These levels are referred to asoccurrence-based levels. As can be seen, in Level 0 the highestoccurring bit portion value has 27369 occurrences; in Level 1 thehighest occurring bit portion value has 12646 occurrences; and in Level2 the highest occurring bit portion value has 3923 occurrences.

In some alternative embodiments, the levels can be defined usingdifferent methods. For example, the occurrences of the BP values may beanalysed in order to determine whether the occurrences can be dividedinto two or more groups in which the total number of occurrences of onegroup (i.e. all occurrence counts in the group summed) of one group isless than or equal to half the total number of occurrences of anothergroup.

If there are only two levels in a bit portion, compression cannot beachieved unless the bit portion is broken up into two or morecombination arrays (see below for description of how bit portions arebroken up into combination arrays). For example, if a bit portion lengthof 4 is used, and two levels are present within the bit portion values,the bit portion can then be broken into two combination arrays. It hasbeen found that one combination array may have one level in its CAvalues, while the other CA may have three levels in its CA values (thisbecomes more likely the longer the bit portion length being used).

Once each of the bit portion values has been assigned to anoccurrence-based level, each of the bit portion values can be assignedan initial label 403. However, in some preferred embodiments the BPvalues are first re-grouped into optimised levels before the initiallabels 403 are assigned. This re-grouping of the BP values intooptimised levels is illustrated in FIG. 5C.

The initial labels are assigned to bit portion values to determine anachievable compression ratio for the processing segment 203, and whethercompression can be achieved at all. They are referred to as “initiallabels” because the actual labels assigned to bit portions may bedifferent once the full compression method as described below is carriedout.

As can be seen in FIG. 5B, the initial labels 403 have varied lengths,but in general bit portion values with a high frequency of occurrencesare assigned a short initial label (e.g. 3 bits long) and bit portionvalues with a low frequency of occurrence are assigned a longer initiallabel (e.g. 5 bits long).

As can also be seen from FIG. 5B, the initial labels 403 can compriseone or two parts: all initial labels 403 comprise a new bit portion (BP)value part; while some initial labels 403 additionally comprise adisambiguation part.

The new values act as primary identifiers of the bit portion values, andall new BP values assigned have the same length in bits—in the exampleshown in FIG. 5B, all new BP values are three bits long. The size inbits of the new BP values is determined by the maximum new BP value. Inthis case the maximum new BP value is 7, which is represented in binaryas 111, and as a result all new BP values comprise three bits. However,if the maximum new BP value was 8, this would be represented in binaryas 1000, and as a results all new BP values would comprise 4 bits.

However, new BP values do not unambiguously identify an associated bitportion value in all cases because in some cases the same new BP valueis assigned to multiple BP values. In such cases, a disambiguation valueis used to identify a particular one of the multiple bit portion valuesassociated with the same new BP value.

In order to ensure that the most frequently occurring bit portion valuesare assigned the shortest initial labels, the bit portion values in thefirst level (Level 0) are each assigned unique new values, as can beseen in FIG. 5B. No disambiguation values are therefore used, and theinitial label assigned to the bit portion values of level 0 onlycomprises the new value part.

When assigning new bit portion values to the bit portion values in level1 onwards, the same new BP values can be assigned to multiple BP values.Where this re-use of new BP values occurs, the number of disambiguationvalues which are needed corresponds to the number of bit portion valueswhich have been assigned the same new bit portion value.

For example, if four bit portion values have been assigned the same newbit portion value, four disambiguation values are required in order tounambiguously identify a particular bit portion value. This means thateach disambiguation value will comprise two bits. It will be appreciatedthat, in general, the higher the number of BP values which are assignedthe same new BP value, the larger the disambiguation value which isassigned to each BP value.

To achieve compression, bit portion values with a high frequency ofoccurrences should generally be assigned a short initial label and bitportion values with a with a low frequency of occurrence shouldgenerally be assigned a longer initial label. Since the new BP valuescomprise the same number of bits for all possible BP values, it is thedisambiguation which principally affects the size of the initial label403.

As a general rule, the lower the level (where Level 0 is the lowest),the fewer BP values are assigned the same new BP value. In thisembodiment, the maximum number of repetitions of a new bit portion valueis set to be 2^(Lev), where Lev is the level of the bit portion valuesbeing assigned new values. For example, in level 2, the same new bitportion value can be assigned to up to 4 bit portion values.

A more general example of new BP value repetition is shown in Table 4,below.

TABLE 4 BP Level New BP Value Level 0 z₀ Level 0 z₁ Level 0 z₂ Level 0z₃ Level 0 z₄ Level 1 z₅ Level 1 z₅ Level 1 z₆ Level 1 z₆ Level 2 z₇Level 2 z₇ Level 2 z₇ Level 2 z₇

As shown in Table 4, each new BP value is repeated 2^(Lev) times. Inlevel 0, new BP values are repeated 2⁰=1 times each. In level 1, new BPvalues are repeated 2¹=2 times each. In level 2, new BP values arerepeated 2²=4 times each.

In FIG. 5B, level 2 comprises 9 BP values. In this level new BP valuescan be assigned to up to four original BP values. Therefore, the fourmost-occurring BP values are assigned the new BP value 5, the next fourmost-occurring BP values are assigned the new BP value 6, and theremaining BP value in Level 2 is assigned the new BP value 7.

In such a situation, as can be seen from FIG. 5B the new BP value 7 isunique, and therefore the least-occurring BP value in Level 2 is notassigned a disambiguation value. This means that the least-occurring BPvalue in Level 2 has an initial label of only 3 bits, while the rest ofthe (more-occurring) BP values in level 2 have initial labels of 5 bits.This is not optimum for compression, and therefore a method of leveloptimisation is used to move BP values between levels, as illustrated inFIG. 5C.

Nevertheless, even without any level optimisation having been performed,it can be seen from FIG. 5B that compression can be achieved. The sizein bits of each occurrence-based label is shown in FIG. 5B, and fromthis the number of bits used to represent the BP values in each levelcan be determined. This is given by the total number of occurrences fora level multiplied by the occurrence-based label size.

The total number of bits used to represent all of the BP values in thebit portion 203 can then be determined by summing the number of bitsused for each level. As shown in FIG. 5B, this is equal to 483555, whichis less than the total number of bits in the processing segment(524288). Accordingly, assuming a header size of 121 bits, a 7.7%compression is possible. In some embodiments, the bit portion length maybe selected based on this possible compression measure, without anyoptimisation of the levels (since compression is achieved withoutoptimisation in some cases).

FIG. 5C illustrates how the bit portion length is selected according topreferred embodiments, where levels are optimised before the potentialcompression is determined.

In FIG. 5C, BP values are first re-grouped into optimised levels beforethe initial labels 403 are assigned. The occurrence-based levelsdetermined in FIG. 5B are indicated on FIG. 5C using dashed braces. Itcan therefore be seen that the optimised levels are generally differentto the occurrence based levels.

A general aim of level optimisation is to ensure that the number N_(Lev)^(BP) of BP values in each level is divisible by 2^(Lev) withoutremainder, where Lev is the level. This ensures efficient use of theassigned new BP values.

This can be represented mathematically as:N _(Lev) ^(BP) mod 2^(Lev)=0  Equation 1

For example, as shown in FIG. 5B, Level 2 includes 9 BP values, soN_(Lev) ^(BP)=9, and for Level 2, Lev=2, therefore the number N_(Lev)^(BP) of BP values in the level is not divisible by 2^(Lev) without aremainder. Specifically:N _(Lev) ^(BP) mod 2^(Lev)=9 mod 2²=9 mod 4=1

The result of N_(Lev) ^(BP) mod 2^(Lev) can be used to indicate how manyBP values should be moved out of the level and into a different level.In this example, one BP value should be moved out of level 2.

In some examples, the condition N_(Lev) ^(BP) mod 2^(Lev)=0 is satisfiedby moving the highest-occurring BP values in the level from level Lev tolevel Lev-1. In the present example, the most-occurring BP value, 1000,is moved from level 2 to level 1.

It will be appreciated that in other examples, the condition N_(Lev)^(BP) mod 2^(Lev)=0 may be satisfied by adding additional BP values tothe level (e.g. the lowest-occurring BP values from level Lev-1 aremoved to level Lev).

In this way, the levels are optimised such that the number of BP valuesin each level is a multiple of 2^(Lev) or equal to 2^(Lev), satisfyingN_(Lev) ^(BP) mod 2^(Lev)=0.

This process of determining whether the number N_(Lev) ^(BP) of BPvalues in a level is divisible by 2^(Lev) is repeated for each level,from the highest level to level 0.

It is noted that for level 0, N_(Lev) ^(BP) mod 2^(Lev) will alwaysequal 0, because 2⁰ is equal to 1. Therefore, the condition N_(Lev)^(BP) mod 2^(Lev)=0 is always fulfilled for level 0, regardless of howmany bit portion values are present in level 0.

Preferred further conditions for optimising bit portion levels aredescribed below.

An initial label 403 is assigned to each BP value based on its level, ina similar way to that shown in FIG. 5B.

The size in bits of each optimised initial label 403 is shown in FIG.5C, and from this the number of bits used to represent the BP values ineach level can be determined. This is given by the total number ofoccurrences for a level multiplied by the optimised initial label size.

As can be seen from FIG. 5B, the total number of bits used to representthe bit portions 205 of the processing segment 203 is 483555 when labelsare assigned to bit portions 205 based on occurrences, without anyoptimisation of the levels. In contrast, as can be seen from FIG. 5C,the total number of bits used to represent the bit portions 205 of theprocessing segment 203 is 470687 when labels are assigned to bitportions 205 using optimised levels. This demonstrates that optimisinglevels results in a higher achievable compression.

FIGS. 5D and 5E illustrate how the achievable percentage compression ofthe processing segment is determined, based on a bit portion lengthL_(BP) of 4 bits and the frequency analysis shown in FIGS. 5B and 5C.

FIG. 5D is a table which summarises the total possible bits used in theheader 211 which is assigned to the compressed portion 209. As shown inFIG. 5D, this calculation is based on the header 211 comprising asignature, and information on the bit portion length, combination arrayconfiguration, and two types of label assignment information—“levelcounts” and “CA value information”. A minimum and maximum size of eachof these parts is determined, and summed in order to provide minimum andmaximum total sizes of the header 211.

FIG. 5E is a table which shows the calculation of the achievablecompression of the processing segment 203 as a percentage of itsoriginal size. The maximum header size is used in this calculation inorder to ensure that the percentage compression is achievable.

As shown in FIG. 5E, the determined achievable compression for theprocessing segment based on a bit portion length L_(BP) of 4 bits is10.20%.

Preferred Conditions for Optimising Bit Portion Levels

In preferred embodiments, in addition to the condition defined byequation 1, level optimisation is based on the following furtherconditions.

Firstly, the number of levels in a bit portion should not exceed the bitportion length:N _(BP) ^(LevelsMAX) =L _(BP)  Equation 2

Secondly, the maximum new bit portion value should equal a targetmaximum new bit portion valueMaxNewBPVal=TargetMaxNewBPVal  Equation 3

Where the target maximum new bit portion value assigned to one or morebit portion values in a processing segment is defined as follows:TargetMaxNewBPVal=2^(└ log) ² ^((N) ^(BP) ^(Levels) ^()┘+1)−1  Equation4

And where the maximum new bit portion value is defined as follows:

$\begin{matrix}{{MaxNewBPVal} = {\left( {\sum\limits_{{Lev} = 0}^{{Lev} = {N_{BP}^{Levels} - 1}}\frac{N_{Lev}^{BP}}{2^{Lev}}} \right) - 1}} & {{Equation}\mspace{14mu} 5}\end{matrix}$L_(BP) is the bit portion length in bits;N_(BP) ^(Levels) is the number of levels into which the bit portionsvalues of a bit portion 205 are divided;N_(BP) ^(LevelsMAX) is the maximum number of levels into which the bitportion values of a bit portion 205 can be divided;Lev is the level index, for example Lev=0 for level 0 and Lev=1 forlevel 1;MaxNewBPVal is the maximum new bit portion value assigned to one or morebit portion values in a processing segmentTargetMaxNewBPVal is the target maximum new bit portion value assignedto one or more bit portion values in a processing segment;N_(Lev) ^(BP) is the number of bit portion values in a level;

Splitting the analysed BP values into more levels, while stillfulfilling the conditions above, typically results in a smaller maximumnew value and therefore smaller initial labels 403 being assigned toeach of the BP values. This allows greater compression to be achieved.

Method of Selecting Configuration of Combination Arrays

FIGS. 6A to 6D illustrate a method of determining which configuration ofcombination arrays 207 to use once a bit portion length L_(BP) has beendetermined according to one or more of the methods described above. Themethod involves dividing the bit portions 205 into combination arrays207 according to different configurations and performing frequencyanalysis on the combination arrays, in order to determine whichconfiguration of combination arrays 207 has the best prospects forcompressing the processing segment 203.

In FIGS. 6A to 6D an exemplary bit portion length L_(BP) of 6 bits isused. As illustrated in Table 2, above, a bit portion 205 having a bitportion length L_(BP) of 6 can be divided up into combination arraysusing 32 different configurations. FIG. 6A provides a visual overview ofhow each bit portion 205 of a processing segment 203 is divided intocombination arrays 207 according to the first 8 combination array (CA)configurations, the 29th CA configuration and the final (32^(nd)) CAconfiguration.

As shown in FIG. 6A, each of the possible CA configurations is assigneda reference number, in this example starting at 0 for the CAconfiguration [1, 1, 1, 1, 1, 1] and continuing to 31 for the CAconfiguration [6, 0, 0, 0, 0, 0].

Frequency analysis is performed on each of the combination array CAconfigurations, and it is determined whether at least one of twocriteria is fulfilled. The first criterion is whether the total numberof levels is greater than or equal to twice the number of arrays. Thesecond criterion is whether, for any of the combination arrays of a CAconfiguration, 50% or fewer of the possible combination array valuesoccur in the processing segment 203. These criteria are explained infurther detail below with reference to FIGS. 6B and 6C. The secondcriterion is whether at least one bit value has an occurrence of 0 (i.e.there are no occurrences of the bit value within the processingsegment).

For the purpose of explanation, the combination array configuration [3,3, 0, 0, 0, 0] (reference number 28) will be considered.

The configuration [3, 3, 0, 0, 0, 0] dictates that each bit portion 205is divided into two arrays, each comprising 3 bits.

As indicated in FIG. 6A, the first array is denoted CA₀, and the secondarray is denoted CA₁.

It will be appreciated that as CA₀ and CA₁ are each 3 bits long, eachcan have any of 8 different combination array values (CA values), as setout in Table 5, below.

TABLE 5 Possible CA₀ values Possible CA₁ values (L_(CA0) = 3) (L_(CA1) =3) 000 000 001 001 010 010 011 011 100 100 101 101 110 110 111 111

For each CA configuration (such as number 28 presently beingconsidered), all equivalent combination arrays in the processing segment203 are analysed collectively. For example, all the CA₀ arrays definedby CA configuration 28 are analysed to determine their values. Thefrequency of occurrence of each possible CA₀ value is determined, fromwhich CA values can be assigned to levels. This is illustrated in FIG.6B. The same analysis is done on all CA₁ arrays, as shown in FIG. 6C.

Considering FIG. 6B in more detail, the number of occurrences of eachpossible CA value is determined (i.e. the frequency of occurrence ofeach CA value within the segment). As shown, the most occurring value010 occurs 30,000 times in the processing segment 203 and the leastoccurring value 100 occurs 3,981 times in the processing segment 203.

The CA₀ values are sorted in order of most occurring to least occurring(as long as more than 50% of the CA₁ values have an occurrence greaterthan 0 within the processing segment). The default order of CA values isfrom smallest to largest, and therefore when two CA values have the samenumber of occurrences within a processing segment (which may be, forexample, zero), the CA values are not sorted and accordingly will remainin size order.

The number of compression levels is then determined based on the numberof occurrences of each of the CA₀ values. In preferred embodiments, a“level” is defined as being a group of bit portion values in which noneof the bit portion values occur less than half as frequently as the mostoccurring bit portion value in that group. In the example shown in FIG.6B, the most occurring CA₀ value occurs 30,000 times, and the secondmost occurring CA₀ value occurs 20,000 times which is more than half of30,000 and therefore both of the most occurring values are assigned tothe same level (Level 0).

The third most occurring CA₀ value, 001, occurs 9,000 times within theprocessing segment 203. Since 9,000 is less than half of 30,000, thethird most occurring CA₀ value 001 is assigned to a second level—Level1.

As 9,000 is the highest occurring value in Level 1, any CA₀ values withan occurrence of less than 4,500 will be assigned to a different level.As shown in FIG. 6B, the sixth most occurring CA₀ value, 110, has anoccurrence of 4,400, and therefore it is assigned to a third level—Level2. No CA₀ values have an occurrence of less than 2,200, and thereforeCA₀ has three levels in total.

Next, considering FIG. 6C in more detail, the second combination arrayCA₁ is analysed in the same way as for CA₀ in FIG. 4B. The frequency ofoccurrence of each possible CA₁ value is determined. As shown, the mostoccurring value 011 occurs 19,000 times in the processing segment 203and the least occurring value 101 occurs 9,000 times in the processingsegment 203.

In a similar way as performed for CA₀, the CA₁ values are sorted inorder of most occurring to least occurring (as long as more than 50% ofthe CA₁ values have an occurrence greater than 0 within the processingsegment).

The number of compression levels is then determined based on the numberof occurrences of each of the CA₀ values.

Using this technique for defining levels, it is found that the totalnumber of levels for CA₁ is two levels.

The total number of levels for the CA configuration 28 [3, 3, 0, 0, 0,0] is therefore 5 levels (3 levels for CA₀+2 levels for CA₁).

Turning back to FIG. 6A, the total number of levels for CA configuration28 can be seen in the “total no. of levels” column. The “2× number ofarrays” column indicates 4 for CA configuration 28 (as there are twocombination arrays), and therefore the first criterion is fulfilled—thetotal number of levels is greater than twice the number of arrays.

It can be seen from FIGS. 6B and 6C that all the possible CA₀ values andall the possible CA₁ values occur in the processing segment 203, andtherefore the second criterion is not fulfilled—for both of thecombination arrays, more that 50% (in fact 100%) of the possiblecombination array values occur in the processing segment 203.

FIG. 6D illustrates that CA configuration 28 is treated at the chosenconfiguration, based on the analysis performed in FIGS. 6B and 6C. Thechosen CA configuration is then used to compress the whole processingsegment 203, by assigning labels to each of the bit portions 205 in theprocessing segment 203, where the labels are generated by splitting thebit portions 205 up into combination arrays 207 in accordance with CAconfiguration 28. This method of compressing the processing segment 203is explained further below.

After the processing segment 203 is compressed using the chosen CAconfiguration, it is checked whether the compression has been successful(e.g. whether any compression has been achieved, or whether thecompression is greater than a predefined threshold). If it is determinedthat compression has not been successful, the method will return toanalysing CA configurations as shown in FIG. 6A, and a new CAconfiguration is chosen for use in compressing the processing segment203.

If none of the possible CA configurations fulfil either of the twocriteria, then the processing segment 203 is not compressed and isoutput by the compression apparatus 105 in its original form.

In some alternative embodiments, if none of the possible CAconfigurations fulfil either of the two criteria, a new bit portionlength is selected using one or more of the methods described above. Anew CA configuration can then be chosen based on the two criteria forselecting CA configurations. In such cases, it is preferable to set aprocessing time limit for attempting to compress a single processingsegment, where expiry of the time limit results in the processingsegment 203 not being compressed and being output by the compressionapparatus 105 in its original form.

Furthermore, if none of the chosen CA configurations are found to resultin successful compression, the processing segment 203 is not compressedand is output by the compression apparatus 105 in its original form.

Why Combination Arrays are Used

In a similar way to assigning levels to bit portions (explained above),the level in which a CA value is placed affects how large thedisambiguation value assigned to the CA value can be.

In preferred embodiments, CA values in the 1^(st) level (level 0) arenot allocated disambiguation values, and therefore all new CA valuesassigned to CA values in Level 0 must be unique.

It is noted that a bit portion having only 2 levels may not be able tobe compressed using only the bit portion, or using a single combinationarray comprising all the bits of the bit portion (unless not all bitportion values, or not all CA values, occur within the processingsegment 203). In such cases dividing the bit portion up into a pluralityof combination arrays can allow compression to be achieved.

It is also noted that the higher the number of levels, the morecompression will be achieved, because the resulting label will besmaller.

Frequency analysis is performed on the bit portions 205. In preferredembodiments, the bit portions 205 are sub-divided up into smallercombination arrays 207 and frequency analysis is also performed on thesecombination arrays 207. For example, the bit portion 205 may be dividedup into a left hand part and a right hand part, such as combinationarrays CA₀ and CA₁ in FIGS. 4B and 4C. The frequency analysis of theleft hand parts (the CA₀ values) allows the most occurring left handpart to be determined. Similarly, the frequency analysis of the righthand parts (the CA₁ values) allows the most occurring right hand part tobe determined.

In preferred embodiments, the labels assigned to the bit portions arenot only dependent on the frequency of occurrence of the whole bitportions, but also on the frequency of occurrence of the combinationarrays which make up the bit portions. Therefore, in the example wherethe bit portion 205 is divided up into a left hand part and a right handpart, the most occurring left hand part of the bit portion will beassociated with the smallest new CA₀ values, and the most occurringright hand part of the bit portion will be associated with the smallestnew CA₁ value. Typically labels generated based on analysis ofcombination arrays will allow greater compression than labels generatedonly based on analysis of bit portions.

Also, breaking up bit portions 205 into combination arrays 207 allowsthe header 211 to use fewer bits. For example, consider a bit portioncomprising 5 bits. Table 6 illustrates two possible CA configurationswhich can be used for a bit portion length of 5 bits—[5,0,0,0,0] and[2,3,0,0,0].

As shown in Table 6, if a CA of length 5 bits is used, the number ofpossible CA values (and BP values as in this case the combination arrayis the same as the bit portion) is:2^(L) ^(CA) =2⁵=32

For each of the 5 combination arrays, in the biggest header format allof the possible CA values are written out in order of occurrence, andtherefore the maximum number of bits used for CA values within header is32*5=160 bits in total.

As shown in Table 6, if two CAs of length 2 and 3 bits are used, thenumber of possible CA values (and BP values as in this case thecombination array is the same as the bit portion) for CA length 2 is:2^(L) ^(CA0) =2²=4

The number of possible CA values (and BP values as in this case thecombination array is the same as the bit portion) for CA length 3 is:2^(L) ^(CA1) =2³=8

For each of the 2 CA₀ combination arrays, in the biggest header formatall of the possible CA values are written out in order of occurrence,and therefore the maximum number of bits used for CA values withinheader is 32*5=160 bits in total.

Table 6 illustrates the maximum number of bits used for CA values withinthe header for the bit portion alone (which can considered as acombination array comprising 5 bits) and for the bit portion beingdivided in to two combination arrays of 2 bits and 3 bits respectively.

TABLE 6 Maximum no. of bits used for CA configuration CA values withinheader [5, 0, 0, 0, 0] 160 bits in total (32 * 5) [2, 3, 0, 0, 0] 32bits in total (4 * 2) + (8 * 3)

As can be seen in Table 6, dividing the bit portion up into combinationarrays results in fewer bit being used for the CA values in the header.

Method of Assigning Labels to Bit Portion Permutations Using CombinationArrays

FIGS. 7A and 7B illustrate a first part of a method of assigning labelsto the permutations of bits represented by bit portions 205, and henceto the corresponding bit portions 205, once a combination array CAconfiguration has been selected according to the method illustrated inFIGS. 6A to 6D.

In this example, the CA configuration 28 [3,3,0,0,0,0] was selected (asshown in FIG. 6D), which means that each bit portion 205 is split upinto two combination arrays—CA₀ and CA₁. FIGS. 7A and 7B illustrate how,for each possible CA₀ value and each possible CA₁ value, a new CA value701 and a disambiguation value 703 is assigned. FIG. 10, describedbelow, illustrates how these new CA values 701 and disambiguation values703 are used to generate labels for bit portion permutations.

The way in which new CA values 701 and disambiguation values 703 areassigned to CA values is similar to the way in which new BP values anddisambiguation values are assigned to bit portion values, as shown inFIGS. 5B and 5C.

As stated above, the level to which a CA value is assigned affects howlarge the disambiguation value 703 assigned to the CA value can be.

The CA₀ and CA₁ values are initially assigned occurrence based levels,as explained above in reference to FIGS. 6B and 6C. However, inpreferred embodiments, before new CA values 701 and disambiguationvalues 703 are assigned, the division of the CA₀ and CA₁ values intolevels is optimised. The optimisation of levels for CA values follows asimilar principle to optimisation of bit portion values, as describedabove.

A general aim of level optimisation is to ensure that the number N_(Lev)^(CA) of CA values in each level is divisible by 2^(Lev) withoutremainder, where Lev is the level. This ensures efficient use of theassigned new CA values and disambiguation values.

This can be represented mathematically as:N _(Lev) ^(CA) mod 2^(Lev)=0  Equation 6

For example, as shown in FIG. 6B, Level 2 includes 3 CA values, soN_(Lev) ^(CA)=3, and for Level 2, Lev=2, therefore the number N_(Lev)^(CA) of CA values in the level is not divisible by 2^(Lev) without aremainder.

Specifically:N _(Lev) ^(CA) mod 2^(Lev)=3 mod 2²=3 mod 4=3

In some examples, the condition N_(Lev) ^(CA) mod 2^(Lev)=0 is satisfiedby moving the highest-occurring CA values in the level from level Lev tolevel Lev-1. In other examples, the condition N_(Lev) ^(CA) mod2^(Lev)=0 may be satisfied by adding additional CA values to the level(e.g. the least-occurring CA value(s) from level Lev-1 are moved tolevel Lev).

In the present example, the least-occurring CA value, 101, from level 1is moved to level 2.

In this way, the levels are optimised such that the number of CA valuesin each level is a multiple of 2^(Lev) or equal to 2^(Lev), thussatisfying N_(Lev) ^(CA) mod 2^(Lev)=0.

This process of determining whether the condition N_(Lev) ^(CA) mod2^(Lev)=0 is satisfied is repeated for each level of each combinationarray, in this example combination arrays CA₀ and CA₁.

As described in relation to optimising levels of BP values, for level 0,N_(Lev) ^(CA) mod 2^(Lev) will always equal 0, because 2⁰ is equal to 1.Therefore, the condition N_(Lev) ^(CA) mod 2^(Lev)=0 is always fulfilledfor level 0, regardless of how many CA values are present in level 0.

Preferred further conditions for optimising CA levels are describedbelow.

In FIGS. 7A and 7B, the levels of CA₀ and CA₁ are optimised using thecondition above and the further preferred conditions for optimisingdescribed below.

Once the levels of CA₀ and CA₁ have been optimised, each of the CA₀ andCA₁ values can be assigned a new CA value 701 and a disambiguation value703.

As described below, where a bit portion permutation is made up of aparticular CA₀ value and a particular CA₁ value, the new CA values 701and disambiguation values 703 associated with the CA₀ value and the CA₁value are combined to generate a label for the bit portion valuerepresented by that permutation.

In a simplified example, the CA₀ value 011 may be assigned a new CAvalue of 2, and a disambiguation value of 1. The CA₁ value 101 may beassigned a new CA value of 3, and a disambiguation value of 2.

To generate a label for the bit portion permutation 011101, the new CAvalues and disambiguation values for the CA values 011, 101 arecombined. Specifically, new CA values 2 and 3 are combined by additionto give a combined new CA value of 5. New disambiguation values 1 and 2are combined by addition to give a combined disambiguation value of 3.The label for bit portion permutation 011101 is created using thecombined new CA value and the combined disambiguation value, so thelabel is 5, 3—which is preferably represented in binary, as 10111. Thebit portion permutation 011101, comprising 6 bits, is thereforerepresented using the label 10111, which comprises 5 bits. As the labelcomprises fewer bits that the bit portion permutation it represents,compression is achieved for all bit portions 205 having bits arranged inthat bit portion permutation.

As can be seen in FIGS. 7A and 7B, new CA values 701 are assigned basedon the level a CA value is in, in a similar way to assigning new BPvalues as described above. As a general rule, the lower the level (whereLevel 0 is the lowest), the fewer CA values are assigned the same(repeated) new CA value. In this embodiment, maximum number ofrepetitions of a new CA value is set to be 2^(Lev), where Lev is thelevel of the CA values being assigned new values. This ensures that themaximum instance of CV values with a high frequency of occurrences issmall and the maximum instance of CV values with a low frequency ofoccurrence is larger. For example, in level 3, the same new CA value canbe assigned to up to 8 CA values.

A more general example of new CA value repetition is shown in Table 7,below.

TABLE 7 CA Value CA Level New CA Value a₀ Level 0 j₀ a₁ Level 0 j₁ a₂Level 0 j₂ a₃ Level 0 j₃ a₄ Level 0 j₄ a₅ Level 1 j₅ a₆ Level 1 j₅ a₇Level 1 j₆ a₈ Level 1 j₆ a₉ Level 2 j₇ a₁₀ Level 2 j₇ a₁₁ Level 2 j₇ a₁₂Level 2 j₇

As shown in Table 7, each new CA value is repeated 2^(Lev) times. Inlevel 0, new CA values are repeated 2⁰=1 times each. In level 1, new CAvalues are repeated 2¹=2 times each. In level 2, new CA values arerepeated 2²=4 times each.

Since the assigned new CA values do not unambiguously identify anassociated CA value in all cases, a disambiguation value is used toidentify a particular one of the multiple CA values associated with thesame new CA value.

The condition that the maximum number of repetitions of a new CA valuein a level is 2^(Lev) ensures that the most frequently occurring CAvalues are assigned the shortest disambiguation values. For example, theCA values in the first level (Level 0) are each assigned unique newvalues, since 2⁰=1 (as can be seen in FIGS. 7A and 7B). Nodisambiguation values are therefore used for CA values in level 0.

When assigning new CA values 701 to the CA values in level 1 onwards,the same new CA values 701 can be assigned to multiple CA values. Wherethis re-use of new CA values 701 occurs, the number of disambiguationvalues 703 which are needed corresponds to the number of bit portionvalues which have been assigned the same new bit portion value.

Considering FIG. 7A in detail, in CA₀ the two CA values in level 0 areassigned new CA values of 0 and 1 respectively. The instance column inFIG. 7A provides a count of new CA values, starting at 0. As can be seenfrom the instance column, there is only a single instance of each of thelevel 0 CA values. Therefore, no disambiguation information is assignedto either of the level 0 CA values.

The two CA values in level 1 are both assigned a new CA value of 2, andtherefore the first (e.g. most occurring) level 1 CA value is assignedan instance value of 0 and the second (e.g. next most occurring) level 1CA value is assigned an instance value of 1. Disambiguation values 703are also assigned to the CA values. In this first combination array,CA₀, the disambiguation values can simply use the instance values, asthere are no previous combination arrays to affect the disambiguationvalues.

In FIG. 7A there are four CA values in level 2, and therefore these CAvalues are all assigned a new CA value of 3, and disambiguation valuesof 0, 1, 2 and 3, corresponding to their instance values.

Considering FIG. 7B in detail, in CA₁ level 0 contains two CA values,and each is assigned a new CA value with a single instance—in this casethe new CA values are 0 and 4 respectively. The two CA values in level 1of CA₁ are assigned a new CA value of 8, while the four CA values inlevel 2 of CA₁ are assigned a new CA value of 12.

The new CA values assigned in CA₀ and CA₁ are selected such that anycombination of new CA values from each of the combination arrays resultsin a unique combined new CA value.

FIGS. 8A to 8D are tables detailing possible combined new CA values withtheir corresponding new CA₀ values and new CA₁ values.

FIG. 8A is a table detailing every possible combination of new CA₀values and new CA₁ values for the example illustrated in FIGS. 7A and7B. As can be seen, the resulting combined new CA values contain norepetitions. Each combined value uniquely identifies a particularcombination of a new CA₀ value and a new CA₁ value—for example thecombined new CA value 7 can only be arrived at by combining new CAvalues 3 and 4 (using addition in this embodiment).

The new CA₀ values are consecutively numbered from 0 to 3, while the newCA₁ values are multiples of 4, from 4*0 to 4*3. As can be seen FIG. 8A,this results in efficient assigning of combined new CA values, becauseall the resulting values are consecutive, thus ensuring that the largestcombined new CA value is as small as it can be (15 in this example).

More generally, the new CA values assigned for a combination array aremultiples of the highest new CA value in the previous array+1.

FIG. 8B shows the combined new CA values for the example illustrated inFIGS. 7A and 7B in binary. The number of binary bits used to representeach of the combined new CA values is based on the size of the maximumcombined new CA value, which in this example is 15. The number 15 isrepresented using four bits in binary (1111) and therefore all combinednew CA values are represented using four bits.

FIG. 8C shows generalised new CA values for CA₀ and CA₁ where CA₀ isassigned new CA values from X₀ to X_(n) and CA₁ is assigned new CAvalues from Y₀ to Y_(n). As can be seen in FIG. 8C, in preferredembodiments the combined new CA values are generated by adding thecorresponding new CA₀ and CA₁ values together.

FIG. 8D shows a further generalised way of assigning combined new CAvalues. In this Figure, each of the combined new CA values are unique(represented by values z₀ to z₁₉), however these values can be generatedusing any method and are not necessarily generated by adding togethernew CA values of CA₀ and CA₁.

As can be seen from FIGS. 7A and 7B, the way in which the disambiguationvalues 703 are assigned for a configuration array depends on thedisambiguation values 703 used in the previous combination array. InFIG. 7B, the “Instance” column shows the same Instances as FIG. 7A.However, the three disambiguation value columns in FIG. 7B show how thedisambiguation values of CA₁ change based on the previous combinationarray, CA₀.

In a similar way as described above in relation to new combinationarrays, the disambiguation values of combination arrays, such as CA₀ andCA₁, are combined to generate a combined disambiguation value.

The disambiguation values assigned in CA₀ and CA₁ are selected such thatany combination of disambiguation values from each of the combinationarrays results in a unique combined disambiguation value. Furthermore,the disambiguation values are preferably selected such that the smallestpossible integers are used as disambiguation values, while stillresulting in unique combined disambiguation values.

This can be seen in FIG. 9, which is a table detailing possiblecombination of CA₀ disambiguation values and CA₁ disambiguation values,and the resulting combined disambiguation values, represented in binary.

As can be seen, the disambiguation values associated with thecombination array depends on the level of the CA values being combined.

If both of the new CA₀ and CA₁ values are in level 0, there are nodisambiguation values to be combined. This means that the resultinglabel for the bit portion permutation corresponding to such CA valueswill include a combined new CA value (in this example comprising fourbits) but will not include a combined disambiguation value. The new CAvalues in level 0 are the most occurring values and therefore thismethod of generating labels ensures that the bit portions comprising themost occurring CA values will be assigned the shortest labels.

In all other instances, FIG. 9 shows the possible CA disambiguationvalues for each combination array and the resulting combineddisambiguation values for the example illustrated in FIGS. 7A and 7B.

As can be seen, the disambiguation values for CA₀ are 0-1 for level 1and 0-3 for level 2. The disambiguation values for CA₁ for level 1 canbe 0-1, 0 and 2, or 0 and 4; while for level 2 the disambiguation valuescan be 0-3; 0, 2, 4 and 6; or 0, 4, 8 and 12.

This ensures that all the resulting combined disambiguation valuescontain no repetitions. Each combined disambiguation value uniquelyidentifies a particular combination of a CA₀ disambiguation value and aCA₁ disambiguation value. Furthermore, as can be seen FIG. 9, thisresults in efficient assigning of combined disambiguation values,because all the resulting combined disambiguation values in each tableof FIG. 9 are consecutive, thus ensuring that for each possiblecombination of disambiguation values the largest combined disambiguationvalue is as small as it can be (maximums may be 1, 11, 111 or 1111 inthis example).

More generally, the disambiguation values assigned for the CA₁combination array are multiples of the highest disambiguation value inCA₀+1 (with the multiples starting at 0).

The number of bits used to represent each combined disambiguation valuedepends on the maximum combined disambiguation value for the levelsbeing combined. For example, combining the disambiguation value 2 fromlevel 2 of CA₀ and 4 from level 2 of CA₁ results in a combineddisambiguation value of 6 which is represented in binary using 4 bits as0110 because the maximum combined disambiguation value for combining CA₀level 2 with CA₁ level 2 is 15 which in binary using 4 bits is 1111. Itcan be seen in FIG. 9 that by adding together the levels associated witheach combination array determines the length, in bits, of the combineddisambiguation values, for example combining CA₀ level 1 with CA₁ level2 results in a 3 bit disambiguation length.

Generally, the higher-occurring the CA values being combined are, thefewer bits will be present in the combined disambiguation value. Asexplained above, the bit portions comprising the most occurring CAvalues will be assigned the shortest labels, in which the labels do notinclude disambiguation information.

FIG. 10 illustrates how labels are assigned to bit portionspermutations, by dividing the bit portion into combination arraysaccording to the chosen CA configuration and combining the new CA valuesand instance values associated with the combination array values of eachof the combination arrays 207 of the bit portion 205.

As shown in the example of FIG. 10, the length of the first part (the“Combined new CA value”) in bits remains constant for all bit portionvalues in a processing segment 203, while the length of the second partcan vary, or the second part may not be used at all to identify some bitportion values.

Advantageously, using labels in which the length of the first part isconstant means that during decompression the labels can be read moreeasily by the decompression apparatus 505, for example requiring lessprocessing power, compared to existing compression methods which uselabels which are based on prefix code alone.

This is because the decompression apparatus does not need to analyseeach individual incoming bit in order to determine the division betweenlabels. Instead, the decompression apparatus 505 can determine from theheader 211 how many bits the first part of each label will comprise (forexample in FIG. 10 the first part always comprises 4 bits, for instance0000). It can also determine from the header how many instance bits (ifany) will follow a first part from the value of the first part itself(e.g. first part 0010 in FIG. 10 is always followed by one bit—either a0 or a 1).

FIG. 11 is a table listing all of the possible bit portions of lengthL_(BP)=6 bits and the labels assigned to each bit portion permutation,based on the combination arrays CA₀ and CA₁ in FIGS. 7A and 7B. As canbe seen, the labels vary in length from 4 bits to 8 bits. The 4 bitlabels are associated with the most occurring combined CA values, whilethe 8 bit labels are associated with the least occurring combined CAvalues. As explained above, the most occurring CA values do not have anydisambiguation value assigned, and therefore the 4 bit labels associatedwith the most occurring combined CA values comprise only the combinednew CA value part, without a combined disambiguation value part.

The labels made up of 5, 6, 7 and 8 bits all comprise a 4 bit combinednew CA value part, along with a combined disambiguation value part whichcomprises 1, 2, 3 or 4 bits respectively.

It is noted that bit portions with 4 bit labels occur approximatelytwice as frequently as bit portions assigned a 5 bit label, four timesas frequently as bit portions assigned a 6 bit label, eight times asfrequently as bit portions assigned a 7 bit label and sixteen times asfrequently as bit portions assigned an 8 bit label. This is because eachadditional bit in the disambiguation value represents an approximatehalving of frequency of occurrence of the combined combination values.This is in turn due to the fact that the disambiguation value assignedto each CA value, for example as shown in FIGS. 7A and 7B, is based onthe level of the CA value, which is determined based on frequencyanalysis. It is noted that the effect of optimising levels means thatthe halving of frequency between successive levels is only approximate.

FIGS. 12A to 12D are examples of generating new CA values (anddisambiguation values) for bit portions having a bit portion lengthL_(BP) of 8 bits, using a CA configuration of [5,3]—a five bitcombination array and a three bit combination array.

FIG. 12A shows the new CA values assigned to the original CA values ofthe 5-bit CA₀ and the 3-bit CA₁ combination arrays. The CA values of the5-bit CA₀ are divided into 3 levels, and the CA values of the 3-bit CA₁are also divided into 3 levels.

FIG. 12A shows that the resulting maximum combined new CA value would be63. This value can be representing in binary using 6 bits, and thereforethe minimum bits label length is 6 bits.

The combined new CA values are generated by combining the twocombination arrays—5-bit CA₀ and 3-bit CA₁—which each have 3 levels, andtherefore the total number of levels in the combination arrays is 6.

FIG. 12B shows the possible disambiguation value lengths (in bits) inrelation to the levels of the CA₀ and CA₁ values being combined in FIG.12A.

It can be seen in FIG. 12B that each disambiguation value length (inbits) is the sum of levels of the CA values being combined—for examplecombining CA₀ level 1 with CA₁ level 2 results in a 3 bit disambiguationlength.

The greatest number of bits used for the combined disambiguation valuescan also be determined by subtracting the number of arrays beingcombined from the total number of levels in the arrays. In this case,the total number of levels in the combination arrays is 6, and thenumber of arrays being combined is two, to the maximum combineddisambiguation length is 4 (6−2=4).

As all combined CA values comprise 6 bits, the label length (in bits) isshown in FIG. 12B as the disambiguation bit length+6.

As can be seen from FIG. 12B, only 3 combinations have an label bitportion length of more bits than the input bit portion length (8 bits),meaning that 67% of the labels are either the same size or smaller thanthe input bit portion length.

In FIG. 12C, the same CA configuration of [5,3] is used, however in thisexample the 5 bit combination array has been changed to use 4 levelsinstead of 3.

FIG. 12C shows that the resulting maximum combined new CA value would be31. This value can be representing in binary using 5 bits, and thereforethe minimum bits label length is 5 bits.

The combined new CA values are generated by combining the twocombination arrays—5-bit CA₀ and 3-bit CA₁—which have 4 and 3 levelsrespectively, and therefore the total number of levels in thecombination arrays is 7.

FIG. 12D shows the possible disambiguation value lengths (in bits) inrelation to the levels of the CA₀ and CA₁ values being combined in FIG.12C.

It can be seen in FIG. 12D that each disambiguation value length (inbits) is the sum of levels of the CA values being combined—for examplecombining CA₀ level 3 with CA₁ level 1 results in a 4 bit disambiguationlength.

The greatest number of bits used for the combined disambiguation valuescan also be determined by subtracting the number of arrays beingcombined from the total number of levels in the arrays. In this case,the total number of levels in the combination arrays is 7, and thenumber of arrays being combined is two, to the maximum combineddisambiguation length is 5 (7−2=5).

As all combined CA values comprise 5 bits, the label length (in bits) isshown in FIG. 12B as the disambiguation bit length+5.

As can be seen from FIG. 12D, only 3 combinations have an label bitportion length of more bits than the input bit portion length (8 bits),meaning that 75% of the labels are either the same size or smaller thanthe input bit portion length.

It is noted that even though the number of levels used for CA₀ in FIGS.12C and 12D has increased from 3 to 4, the maximum label size in bitsremains at 10 bits.

Preferred Conditions for Optimising CA Levels

In preferred embodiments, level optimisation is based on the followingconditions.

Firstly, the number of levels in a combination array should not exceedthe combination array length:N _(CA) ^(LevelsMAX) =L _(CA)  Equation 7

Secondly, the number N_(Lev) ^(CA) of combination array values in eachlevel should be divisible by 2^(Lev)N _(Lev) ^(CA) mod 2^(Lev)=0  Equation 8

Thirdly, the maximum new combination array value assigned to one or morevalues in a combination array should equal a target maximum newcombination array value.MaxNewCAVal=TargetMaxNewCAVal  Equation 9

Where the target maximum new combination array value assigned to one ormore combination array values is defined as follows:TargetMaxNewCAVal=2^(└ log) ² ^((N) ^(CA) ^(Levels) ^()┘+1)−1  Equation10

And the maximum new combination array value is defined as follows:

$\begin{matrix}{{MaxNewCAVal} = {\left( {\sum\limits_{{Lev} = 0}^{{Lev} = {N_{CA}^{Levels} - 1}}\frac{N_{Lev}^{CA}}{2^{Lev}}} \right) - 1}} & {{Equation}\mspace{14mu} 11}\end{matrix}$L_(CA) is the combination array length in bits;N_(CA) ^(Levels) is the number of levels into which the combinationarray values of a combination array 207 are divided;N_(CA) ^(LevelsMAX) is the maximum number of levels into which thecombination array values of a combination array 207 can be divided;Lev is the level index, for example Lev=0 for level 0 and Lev=1 forlevel 1;MaxNewCAVal is the maximum new combination array value assigned to oneor more combination array values in a processing segmentTargetMaxNewCAVal is the target maximum new combination array valueassigned to one or more bit portion values in a processing segment;N_(Lev) ^(CA) is the number of combination array values in a level;

However, in some situations not all conditions can be met. For example,if only two levels are present, it may not be possible for the maximumnew combination array value to equal the target maximum new combinationarray value, but all other conditions can be met. In such situations,for example in FIG. 5B, the combination array configuration can still beused, as compression is still achievable.

Splitting the analysed CA values into more levels generally results in asmaller maximum new value and ultimately smaller labels being assignedto bit portions.

Hard-to-Compress Data

It is possible to achieve compression using the above described methodseven if the frequency of occurrence of BP/CA values is substantiallyeven across all possible BP/CA values and thus all BP/CA values exist inthe same level, so long as at least one of the BP values and/or CAvalues has an occurrence of 0.

Compression can be achieved in such cases by assigning one of the BPvalues and/or CA values to a different level (e.g. assigning the firstBP/CA value to level 0 and all others to level 1). This causes the firstBP and/or CA value to be assigned fewer disambiguation value bits thanthe remaining BP and/or CA values (for example, the BP value in level 0may not be assigned a disambiguation value, and as a result the labelassigned to the BP value in level 0 will be 1 bit in length smaller thanthe BP values in level 1).

For example, using a bit portion length of 8 bits, the BP value of level0 is assigned a new BP value of 0000000 with no disambiguationinformation. This level 0 BP value is therefore assigned a label whichis 7 bits long; 1 bit shorter than the original 8 bits of the BP value.The remaining BP values are assigned new BP values of 1-127(0000001-1111111), each with a disambiguation value of either 0 or 1.Therefore, the level 1 new BP values are assigned labels with 8 bits,which is the same number of bits as the original BP values.

Due to the relatively small size of the header in most situations,compression can still be achieved even if BP/CA value in level 0 hasexactly the same number of occurrence as all other BP/CA values. Thisoccurs more often when using smaller bit portion lengths because theoccurrence values are higher and offset the header size. It is notedthat that the BP/CA values are preferable not sorted to achieve thiscompression, in order to avoid having to use a larger header to indicatehow the BP/CA values have been sorted. Therefore, the BP/CA valueassigned to a different level (e.g. level 0) need not be the mostoccurring.

It should be noted that for each additional BP value and/or CA valuethat is not in use (occurrence is 0), the compression which can beachieved increases.

For example, if two BP/CA values have an occurrence of 0, the first twoBP/CA values can be assigned to level 0 and the remaining BP/CA valueswould be assigned to level 1. The result would be that the two new BP/CAvalues in level 0 are assigned labels which are 1 bit shorter than theoriginal BP/CA values. All other BP/CA values would be assigned labelswhich are the same length as the original BP/CA values.

For a substantially evenly distributed processing segment, new BP/CAvalues and disambiguation values are assigned in the same way, until thepoint at which 50% of the available BP/CA values be not in use. At thispoint, all BP/CA values can be assigned to level 0 and compression canstill be achieved.

If more than 50% of the BP/CA values have an occurrence of 0, highercompression can be achieved by assigning new BP/CA values anddisambiguation values are in the same way as described above. At thispoint each additional BP/CA value with an occurrence of 0 can beassigned to level 0, resulting in labels which are two bits shorter. Theother BP/CA values can be assigned to level 1, resulting in labels whichare one bit shorter than the original BP/CA values.

TABLE 10 Number of BP/CA values No. of bits saved No. of bits withoccurrence greater than 0 in level 0 saved in level 1 128 to 255 1 bitless 0 bit less  64 to 127 2 bits less 1 bit less 32 to 63 3 bits less 2bits less 16 to 31 4 bits less 3 bits less  8 to 15 5 bits less 4 bitsless 4 to 7 6 bits less 5 bits less 1 to 3 7 bits less 6 bits less

Table 10 shows the number of bits saved for a BP/CA with length of 8bits, depending on the number of BP/CA values with occurrence greaterthan 0.

TABLE 11 Number of Bit Portion different possible Expected number ofoccurrences of Length BP values (i.e. each possible BP value in a (inbits) permutations of bits) processing segment of length 64 KB 1 2262144 2 4 65536 3 8 21846 4 16 8192 5 32 3277 6 64 1366 7 128 586 8 256256 9 512 114 10 1024 52 11 2048 24 12 4096 11 13 8192 5 14 16384 3 1532768 2 16 65536 1

Table 11 shows the expected number of occurrences of each possible BPvalue in a processing segment of length 64 KB, where all possible BPvalues occur in the processing segment, and the frequency of occurrenceof BP values is substantially even across all possible BP values. Forexample, if the bit portion length is 1, each of the possible BP values(0 and 1) would be expected to occur 262144 times in the processingsegment of 65536 bytes (64 KB).

In the case where the frequency of occurrence of BP values issubstantially even across all possible BP values, but at least one BPvalue does not occur, the number of bits which can potentially be savedis the expected number of occurrences of each possible BP value in aprocessing segment shown in Table 11 above (less the size of theheader). For example, for a bit portion length of 3, if only 7 of the 8possible BP values occur in the processing segment, 21846 bits couldpotentially be saved (less the size of the header). As long as theheader does not exceed the expected number of occurrences, compressioncan be achieved.

The expected number of occurrences of each possible BP value in Table 11is given by:

$\begin{matrix}{N_{occurrences}^{BP} = \frac{L_{PS}}{L_{BP} \times 2^{L_{BP}}}} & {{Equation}\mspace{14mu} 12}\end{matrix}$Where:L_(PS) is the processing segment length in bits.

Table 12 shows the expected number of occurrences of each possible CAvalue for a bit portion length of 4 bits.

TABLE 12 Number of Expected number different possible of occurrences ofBit Portion Combination CA values (i.e. each possible CA Length (inarray Length permutations of value in a processing bits) (in bits) bits)segment of length 64 KB 4 1 2 65536 4 2 4 32768 4 3 8 16384 4 4 16 8192

The possible CA configuration for a bit portion length of 4 are[1,1,1,1], [1,1,2,0], [1,2,1,0], [1,3,0,0], [2,1,1,0], [2,2,0,0],[3,1,0,0], [4,0,0,0]. Therefore, as shown in Table 12, CA lengths of 1,2, 3 and 4 are possible.

Table 12 shows the expected number of occurrences of each possible CAvalue in a processing segment of length 64 KB, where all possible CAvalues occur in the processing segment, and the frequency of occurrenceof CA values is substantially even across all possible CA values. Forexample, if the CA length is 1, each of the possible CA values (0 and 1)would be expected to occur 65536 times in the processing segment of65536 bytes (64 KB).

In the case where the frequency of occurrence of CA values issubstantially even across all possible CA values, but in one combinationarray at least one CA value does not occur, the number of bits which canpotentially be saved is the expected number of occurrences of eachpossible CA value in a processing segment shown in Table 12 above (lessthe size of the header). For example, for a CA length of 3, if only 7 ofthe 8 possible CA values of a combination array occur in the processingsegment, 16384 bits could potentially be saved (less the size of theheader). As long as the header does not exceed the expected number ofoccurrences, compression can be achieved.

The expected number of occurrences of each possible CA value in Table 12is given by:

$\begin{matrix}{N_{occurrences}^{CA} = \frac{L_{PS}}{L_{BP} \times 2^{L_{CA}}}} & {{Equation}\mspace{14mu} 13}\end{matrix}$Header Structure

FIGS. 13A to 13D are simplified representations of four exemplary headerstructures.

As stated previously, preferably, each header starts with a compressionmethod signature, and provides information relating to the chosen bitportion length L_(BP), the combination array configuration used, thesize of the original processing segment 203, and information on howlabels were assigned to each of the bit portions 205. FIGS. 13A to 13Dare preferred header structures.

Header Format 0

As shown in FIG. 13A, the header starts with a signature. In thisexample, the signature is referred to as a “SISP” signature, which is anexemplary trade name for the presently described compression method. The“SISP” signature is 32 bits long.

The header also specifies the bit portion length L_(BP), which in thisexample is allocated 4 bits in the header (and therefore, in thisexample, the bit portion length L_(BP) can be a maximum of 16 bits). Thenumber of bits allocated to the bit portion length L_(BP) in the headermay be CPU dependent.

The CA configuration is also specified in the header, which uses L_(BP)bits. Preferably, the CA configuration is specified by its referencenumber, which (in combination with knowledge of the bit portion lengthL_(BP)) unambiguously identifies the CA configuration used to assignlabels to the bit portions 205.

Furthermore, the size of the processing segment (in bytes) is specified,and in this embodiment the processing segment size can be between 0 and65535 bytes because the length of the processing segment size part ofthe header is 16 bits as shown.

Also, in preferred embodiments, multiple different header formats can beused (e.g. 3). The header format can be chosen based on which willresult in the smallest total header size for a processing segment.Therefore, the header includes a part comprising two bits for indicatingthe header choice.

As described above, the CA configuration may use any number of arrayswithin a range, where the range is from one array to L_(BP) arrays(L_(BP) arrays would occur when all arrays are one bit in size). Theheader contains information relating to each of the combination arraysin the combination array configuration, and therefore as a minimum theheader will contain CA₀ information if only one array is used by the CAconfiguration.

In the example given in FIG. 13A, the CA configuration uses more thanone array, and therefore the combination array information comprises CA₀information through to CA₁ information.

As shown, the CA₀ information comprises a count for each of levels 0 toL_(CA0), where the count indicates how many CA₀ values are present inthe respective level. The count can be from level 0 to level L_(CA0)because the maximum number of levels in a combination array is length ofthe combination array in bits (L_(CA)).

The CA₀ information further comprises a single bit indicator to indicatewhether the CA₀ values are sorted (e.g. by frequency of occurrence).

The CA₀ information also comprises frequency of occurrence information,which indicates the rankings of CA₀ values and whether they are in use.

Specifically, if the CA values have been sorted, all possible CA valuesare written out in order of occurrence, including any CA values havingan occurrence of 0.

If the CA values have not been sorted, then a single bit for eachpossible CA value is written out in unsorted order, where a value of 0represents no occurrences of the CA value and a value of 1 representsone or more occurrences of the CA value.

As shown, the CA_(n) information comprises the same information fieldsas the CA₀ information.

It will be appreciated that equivalent information as the CA₀information and CA_(n) information will be included in the header forany intervening combination arrays present in the CA configuration.

Header Format 1

FIG. 13B illustrates header format 1. The header format contains thesame parts as header format 0, with the exception of the frequency ofoccurrence information.

Specifically, for header format 1, if the CA values have been sorted, asingle bit for each possible CA value is written out in unsorted order,where a value of 0 represents no occurrences of the CA value and a valueof 1 represents one or more occurrences of the CA value. In addition tothe occurrence indicating bit, additional bits may be included afterthis bit, depending on whether or not the occurrence is 0 and whetherthe CA value has been swapped with another CA value. If the occurrenceof a CA value is greater than 0 and the CA value has not been swapped, a“swap indicator” bit of 0 is included after the occurrence indictor bit.If the occurrence of a CA value is greater than 0 and the CA value hasbeen swapped, then a “swap indicator” bit is included after theoccurrence indictor bit along with the swapped CA value assigned to theCA value (where the swapped CA value is represented in bits).

If the CA values have not been sorted, then a single bit for eachpossible CA value is written out, where a value of 0 represents nooccurrences of the CA value and a value of 1 represents one or moreoccurrences of the CA value.

As shown, the CA_(n) information comprises the same information fieldsas the CA₀ information.

It will be appreciated that equivalent information as the CA₀information and CA₁ information will be included in the header for anyintervening combination arrays present in the CA configuration.

Advantageously, header format 1 does not write out every possible CAvalue in the CA value frequency of occurrence information part, andtherefore the resulting header is smaller.

Header Format 2

FIG. 13C illustrates header format 2. The header format contains thesame parts as header format 1, with the exception that the frequency ofoccurrence information specifies the first occurring CA value (in thisexample 000) and the last occurring CA value (in this example 110), andthat no information on CA values before and after these first and lastoccurring CA values is included.

As shown, the CA₁ information comprises the same information fields asthe CA₀ information.

It will be appreciated that equivalent information as the CA₀information and CA₁ information will be included in the header for anyintervening combination arrays present in the CA configuration.

Advantageously, header format 2 does not write out every possible CAvalue in the CA value frequency of occurrence information part, andtherefore the resulting header is smaller.

Header Format 3

FIG. 13C illustrates header format 3. This header format contains thesame parts as the other header formats, with the exception that nofrequency of occurrence information is included. This means that headerformat 3 is preferably only be used if all CA₀ to CA₁ values occur inthe processing segment and no sort has occurred.

It is noted that the headers advantageously don't require the mappingbetween each CA (or BP) value and the corresponding new value (or label)to be written out specifically. The header instead just indicates howthe permutations (BP or CA values) have been grouped, which allows adecompression apparatus to determine the mapping between each CA (or BP)value and the corresponding new value (or label).

Reprocessing Processed Segments and Associated Headers

Once all of the bit portions 205 of a processing segment 203 have beenassigned labels, a processed segment 209 is output in which the bitportions are represented using their respective labels. A header 211 isoutput with the processed segment 209 in order to allow the processedsegment 209 to be decompressed (e.g. by a decompression apparatus 505).

In preferred embodiments, the processed segment 209 and associatedheader 211 are then reprocessed, using the methods described above,treating the processed segment 209 and associated header 211 as a newprocessing segment 203. The reprocessing of the processed segment 209and associated header 211 results in the generation of a new processedsegment 209 and new associated header 211, where the total size in bitsof the new processed segment 209 and new associated header 211 is lessthan the total size in bits of the processed segment 209 and associatedheader 211.

In alternative embodiments, the compressed file 202 is reprocessed, bydividing the compressed file 202 into new processing segments andperforming the methods for compression described above.

Although it is recognised that generally it is not possible torecompress data using the same compression method, the methods ofcompression described in this application advantageously allow the wayin which data is processed to be significantly varied, on the fly, (forexample changing bit length, changing CA configuration, changinggrouping (levels) of permutations (BP or CA values), in order to allowdata to be recompressed at least once.

Alternative Method for Calculating Target Maximum BP Values and CAValues

FIG. 14 illustrates the target maximum BP and/or CA values calculated inaccordance with an alternative embodiment. In this embodiment, thetarget maximum new bit portion value assigned to one or more bit portionvalues in a processing segment is defined as follows:TargetMaxNewBPVal=2^(└ log) ² ^(└ log) ² ^((N) ^(BP) ^(Present)^()┘┘+1)−1  Equation 14N_(BP) ^(Present) is the number of bit portions with an occurrencegreater than 0 in the processing segment.

Similarly, in this embodiment, the target maximum new CA value assignedto one or more CA values in a processing segment is defined as follows:TargetMaxNewCAVal=2^(└ log) ² ^(└ log) ² ^((N) ^(CA) ^(Present)^()┘┘+1)−1  Equation 15N_(CA) ^(Present) is the number of combination arrays with an occurrencegreater than 0 in the processing segment.

As can be seen from FIG. 14, in an example where only 255 out of 256possible bit portions are present in a processing segment (where a bitportion length L_(BP)=8 is being used), the target maximum BP value is 7rather than 15. Since 7 can be represented in binary using one less bitthan 15, this means that the label assigned to the most occurring BPvalues will be one bit less.

This method of calculating the target maximum new bit portion value canadvantageously achieve higher levels of compression; however it mayrequire additional manipulation of the levels. For example, in somecases the number of levels determined using frequency analysis will betoo low to achieve the target maximum new bit portion value. Therefore,in some embodiments where this method of calculating the target maximumnew bit portion value is used the bit portions values may be dividedinto levels using methods different to those described above.

Combination Array Configuration Reference number

As explained above, a processing segment 203 may be divided into aseries of bit portions 205, and each bit portion may be divided into aplurality of combination arrays 207 according to a CA configuration.

For ease of understanding a particular CA configuration can berepresented by a series of numbers within brackets, where each numberrepresents the size of a combination array in bits, and where 0indicates that no array is used. For example, {1, 1, 3, 1, 0, 0} denotesa bit portion 205 with a bit length of 6 comprising four combinationarrays 207, the first two combination arrays comprising a single biteach, followed by a 3 bit combination array, in turn followed by anothersingle bit array. The sum of the numbers within the brackets dictatesthe bit length associated with the CA configuration.

Another way of representing a particular CA configuration is using avisual mask which visually shows then number of arrays, with each arraydepicted using a pair of square brackets and the number of elements inthe array depicted using one or more letters (in this case the letterx). Accordingly, CA configuration {1, 1, 3, 1, 0, 0} can be representedas [x][x][xxx][x].

Generally, a CA configuration defines a repeating pattern of one or morearrays which repeats every L_(BP) bits.

It will be appreciated that a bit portion 205 which is not subdivideinto combination arrays can also be represented as a CA configurationmade up of a single array. For example, a bit portion having a bitlength L_(BP) of 3 can be considered a CA configuration of {3,0,0}.

FIG. 15 illustrates a number of CA configurations and associatedreference numbers, or indexes. FIG. 15 shows CA configurations from CAconfiguration {1} which corresponds to a single array with a bit lengthof 1 to CA configuration {16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} whichcorresponds to a single array of has a bit length of 16.

As shown in FIG. 15, each CA configuration is assigned a referencenumber which uniquely identifies the CA configuration, referred to asthe CAref (or the “SISP Number”). CA configuration {1} is assigned aCAref of 1, while CA configuration {16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0} isassigned a CAref of 65535.

In order to map a CAref to a CA configuration, the binary representationof the CAref is used. As shown in FIG. 15, the conventionalrepresentation of binary is used, where leading zeros are omitted. Thebinary bits of each binary CAref indicate how to divide a series of bitsinto combination arrays.

Specifically, the number of bits in the CAref corresponds to the bitlength, which dictates how many bits are included in each bit portion.The division between combination arrays within the bit portion isindicated by a change in value of adjacent bits (from a 0 to a 1 or viceversa). Therefore, the number of bits assigned to each combination arrayis indicated by how many consecutive bits have the same value.

The binary CAref can therefore be used as a mask itself which can beused to control how a bit stream is broken up into arrays.

For example, CAref 25 is 11001 in binary, which represents CAconfiguration {2, 2, 1, 0, 0}—i.e. a CA configuration of three arrays,comprising two bits, two bits and 1 bit respectively. This can also berepresented as a visual mask, as shown in FIG. 15, as [xx][xx][x].

The bit length L_(BP) of a CA configuration can also be determined fromits decimal CAref directly, without converting the CAref into its binaryform. The CAref can be directly mapped to the L_(BP) using equation 16:L _(BP)=└ log₂(CAref)+1┘  Equation 16Alternative Example of Dividing Data into Portions

It will be appreciated that, depending of the data being processed,certain CA configurations and bit lengths will provide better prospectsfor compression than others. In some examples described above, the bitlength is first selected and the bit portions are then divided intocombination arrays. However, in examples described below, the selectionof bit length and combination array configuration is made simultaneouslyby analysing different combination array configurations with various bitlengths.

FIG. 16 illustrates a method of determining which configuration ofcombination arrays 207 to use to divide up a processing segment 203. Themethod involves dividing processing segment 203 into combination arraysaccording to each CA configuration and performing frequency analysis onthe combination arrays, in order to determine which configuration ofcombination arrays 207 has the best prospects for compressing theprocessing segment 203. As shown in FIG. 16, CA configurations of anybit length (in this case up to and including 16 bits) are analysed andthe CA configuration with the best prospects for compression isselected.

In FIG. 16, instead of first selecting a bit length (using frequencyanalysis as explained above) and then splitting the bit length up intodifferent combination array configurations in order to determine asuitable CA configuration, in this alternative example every differentCA configuration of every bit length is tested until a CA configurationwhich fulfils a predetermined processing criterion is identified.

The predetermined processing criteria which are considered areequivalent those described above with reference to FIG. 6A. The firstcriterion is whether the total number of levels is greater than or equalto twice the number of arrays. The second criterion is whether, for anyof the combination arrays of a CA configuration, 50% or fewer of thepossible combination array values occur in the processing segment 203.In other words, the second criterion is whether at least one bit valuehas an occurrence of 0 (i.e. there are no occurrences of the bit valuewithin the processing segment).

It will be appreciated that when the combination arrays of a processingsegments have similar frequencies of occurrence for every possible CAvalue, in other words where a processing segment has a relatively evendistribution when divided up based on a particular CA configuration, thepredetermined processing criteria are less likely to be fulfilled.Conversely, the more uneven the distribution of possible CA values, themore likely it is that a predetermined processing criterion will befulfilled.

The method illustrated in FIG. 16 generally requires more processing tobe performed before a CA configuration is selected, when compared to themethod illustrated in FIGS. 6A to 6D. However, the method illustrated inFIG. 16 has the advantage that the selected CA configuration has thesmallest possible bit length. Selecting a bit length before testing anyCA configurations can mean that CA configurations with sufficientlyuneven distributions can be missed if the distribution of the associatedbit length happens to be even. For example, even if a segment split upinto bit portions of bit length L_(BP)=7 has even distribution, a CAconfiguration of {3,1,3} may still have a very uneven distribution.

It is noted that, in this example, the bit portion module 253 isconfigured to split processing segments into combination arrays.

Further Exemplary Header Structure

FIG. 17 is a simplified representation of a further exemplary headerstructure for use where a CAref is used to identify the CA configuration(and bit length) used in the processing of segments.

The header structure illustrated in FIG. 17 is based on the headerstructure shown in FIG. 13A, with some modifications, and is thereforereferred to as Header Format 0′.

Unlike the header structure shown in FIG. 13A, the Header Format 0′ doesnot include a “SISP” signature in order to decrease the size of theheader.

As shown in FIG. 17, the header starts with specifying the CAConfiguration used to process the processing segment, preferably in theform of a CAref as described above.

Furthermore, the size of the original processing segment (in bytes) isspecified, and in this embodiment the processing segment size can bebetween 0 and 65535 bytes because the length of the processing segmentsize part of the header is 16 bits (though segments can be any size sothis part of the header is not limited to 16 bits). In some examples theoriginal processing segment size is not included.

Next, the header specifies the size of the compressed segment in bits.It is advantageous that the segment size is specified in bits becauseonce a segment has been processed it does not necessarily includes around number of bytes (i.e. the number of bits may not be a multiple of8) and therefore specifying the processed segments length as a number ofbit means that the end of the current processed segment and the start ofthe next one can be determined. It is noted that the size of thecompressed segment in bits may or may not include the header size.

The other information provided in header format 0′ is equivalent to thatdescribed with relation to FIG. 13A, and will therefore not be describedfurther here.

Segment Marker

Each processing segment 203 will have particular characteristics—forexample some segments will have large variations in data and some willhave very little variation. The distribution of byte value occurrences(and/or bit portion value occurrences and/or combination array valueoccurrences) can be very even, or can be very uneven. Furthermore, somewill have many bytes having low values and some will have many byteshaving high values. In preferred examples, segments are assigned segmentmarkers, herein referred to as segmarks (also referred to as SISPSignatures), where the segmark reflects to particular characteristics ofthe segment. In other words, the segmark represents the distribution ofdata values within a segment.

The segmark assigned to a processing segment 203 can be used as apointer to a table identifying one or more CA configurations which arelikely to provide good compression of the segment. Preferably, the tableof CA configuration choices comprises one or more combination arrayconfiguration references (CArefs, also referred to as SISP numbers) toidentify corresponding CA configurations which are likely to providegood compression of the segment. Furthermore, the segmark can be used toidentify processing segments with similar characteristics, in turnidentify CA configurations which achieved compression of these similarprocessing segments.

Segmarks comprise one or more values which reflect the data within aprocessing segment, such as the average byte value in the segment. Thesegmark can be single or multi-dimensional. FIGS. 18a, 18b, 19a-19d and20a-20c illustrate how an exemplary segmark is determined, where thesegmark comprises three values—the average byte value, the averagechange in byte value and the average change in occurrence of byte valuewithin the processing segment.

FIGS. 18a and 18b show extracts from an exemplary 65536 byte processingsegment. Specifically, FIG. 18a shows the values of the first 55 bytes(of the 65536 bytes in the segment), along with the change in value ofeach of these bytes with respect to the preceding byte. As the byte inposition 0 has no preceding byte, the change in byte value is notapplicable. The change in byte value is an absolute value.

In alternative examples, the change in byte value for the byte inposition 0 is determined to be the same as the value of the byte inposition 0. In further alternative examples, the value of the change inbyte value for the byte in position 0 is 0, or half of the value of thebyte in position 0.

FIG. 18b shows the number of occurrences of byte values within thesegment, along with the change in number of occurrences of the bytevalues with respect to the preceding byte. For simplicity, FIG. 18b onlyshows the first 16 and last 6 byte values (0-15 and 250-255). As bytevalue 0 has no preceding byte value, the change in number of occurrencesis not applicable. The change in number of occurrences is an absolutevalue.

FIGS. 19a, 19b, 19c and 19d are graphs plotting the data shown in thetables of FIGS. 18a and 18 b.

Specifically, FIG. 19a is a line graph plotting each byte value withinthe segment, against its position within the segment. The average bytevalue in the processing segment is 127.594, which is indicated by thedashed line in FIG. 19a . For simplicity, FIG. 19a only shows the bytevalues of the first 55 byte positions.

FIG. 19b is a line graph plotting the change in each byte value withinthe segment, against its position within the segment. The average changein byte value in the processing segment is 85.076, which is indicated bythe dashed line in FIG. 19b . For simplicity, FIG. 19b only shows thechange in byte values of the first 55 byte positions.

FIG. 19c is a bar graph plotting the number of occurrences of each bytevalue within the segment. The average occurrence is 256, which isindicated by the dashed line in FIG. 19c . For simplicity, FIG. 19c onlyshows the number of occurrences of the first 55 byte values.

It will be appreciated that, regardless of the number of times eachparticular byte occurs within a segment, the average byte valueoccurrence will always be the same for segments of the same size. Forexample, for a processing segment comprising 65536 bytes, the averagebyte value occurrence will always be 256 no matter what distribution.For this reason, the average byte value occurrence is not used togenerate a segmark. However, the average change in occurrence can varygreatly between segments of the same size, and therefore thismeasurement is suitable for use in the generation of the segmark.

FIG. 19d is a bar graph plotting the change in number of occurrences ofeach byte value within the segment. The average change in occurrence is19.761, which is indicated by the dashed line in FIG. 19d . Forsimplicity, FIG. 19d only shows the change in number of occurrences ofthe first 55 byte values.

FIGS. 20a to 20c are schematic diagrams illustrating a simplifiedoverview of how a segmark is generated. In this example, the wholeprocessing segment is analysed to determine the average byte value, theaverage change in byte value, and the average change in byte valueoccurrence.

In FIG. 20a , the determination of these three values is representedusing three graphs. The leftmost graph is a plot showing byte value foreach byte position, with the average byte value of 127.594 depictedusing a dotted line. The middle graph is a plot showing the change inbyte value for each byte position, with the average change in byte valueof 85.076 depicted using a dotted line. The rightmost graph is a plotshowing the change in byte value occurrence for each byte value, withthe average change in byte value occurrence of 19.761 depicted using adotted line.

It is noted that two segments can have the same segmark (for example,this could occur if the bytes of the processing segment have the sameoccurrence but the order is different).

FIG. 20b shows normalisation of each of the three values used in thesegmark. Normalisation is performed to make storage and comparison ofsegmarks easier, and can involve weighting and/or rounding of the threevalues. In this example, each of the values is multiplied by 1000 andconverted to an integer. The number is normalised to three decimalplaces in order to generate a sufficiently accurate segmark, whichminimises the number of segments which have the same segmark.

In this example, the total number of different possible segmarks is1.66464*10{circumflex over ( )}16, which is a 17 digit number. The totalnumber of different possible 16 bit segments is 2{circumflex over( )}524288, which is approximately a 157800 digit number. Therefore, itcan be seen that multiple processing segments can have the same segmark.

FIG. 20c is a visual representation of a three dimensional matrix madeup of the three parameters used to define the segmark (average bytevalue, average change in byte value, average change in occurrence). InFIG. 20c , each of the three parameters are normalised as integers asshown in FIG. 20b . Therefore the three dimensional matrix comprises anelement for every possible segmark. When a processing segment iscompressed, a pointer to the CA configuration used to achievecompression can be stored in the element of the three dimensional matrixcorresponding to the segmark. Additionally or alternatively, the CArefof the CA configuration is stored in the three dimensional matrix.

It is noted that a three dimensional matrix is one option for storinginformation relating to segmarks; however this information can be storedin any suitable way, such as in a one dimensional array.

In FIG. 20c the normalised average byte value is shown on the x axis,with a range from 0 (which would occur if every byte in the segment hada value 0) to 255000 (which would occur if every byte in the segment hasa value 255).

The normalised average byte change value is shown on the y axis, with arange from 0 (which would occur if all bytes in the segment have thesame size) to 255000 (which would occur if every byte in the segmentchanged the maximum amount with respect to the previous value—e.g. ifthe byte of the segment were 0, 255, 0, 255, 0, 255 . . . ).

The normalised average occurrence change value is shown on the z axis,with a range from 0 (which would occur if all bytes in the segment hadthe same occurrence) to 256000 (which would occur if the segment doesnot include any two consecutive byte values with a non-zero occurrence,or in other words if at least every other byte has an occurrence of 0).For a segment to have a normalised average occurrence change value of256000, at least half of the bytes in the segment must have anoccurrence of 0.

In FIG. 20c , the matrix element at position (127594, 85076, 19761) isrepresented using a solid back dot in the three-dimensional space. Whenthe exemplary processing segment shown in FIGS. 18a and 18b iscompressed, the CA configuration reference number (CAref) used toachieve compression is stored in the matrix element at position (127594,85076, 19761).

In other non-limiting examples, values such as segment length, lowestbyte value present in the segment, highest byte value present in thesegment, standard deviation of byte values in the segment, one or moreFourier analysis coefficients of the bytes in the segment, may be usedin addition to or instead of the three segmark values described above.Furthermore, the average deviation of byte value occurrences from themean byte value occurrence can be used. Yet further, the coefficient ofvariation of bit portion (or combination array) occurrences can be used.

In this example, the segmark is generated by analysing the segment basedon splitting the data into bytes. This is useful as bytes are a standardunit for processing binary data. However, it will be appreciated thatthe segmark can be generated based on analysis based on splitting thedata into portions other than bytes—for example 16 bit portions (aka aword).

Although in this example the segmark is generated from analysing all ofthe bytes present in the segment, it will be appreciated that a sampleof the bytes can instead be analysed.

Normally, CA configurations are tested in order of their CAref, fromlowest to highest. However, if it is determined that one or moreneighbouring segmarks have a successful CA reference number assigned,then the order in which CA configurations is tested is changed such thatthe successful CA configurations associated with neighbouring segmarksare tested first.

Using Segment Marker to Point to CA Configuration Table

FIGS. 21a to 21h illustrate the process of populating the threedimensional segmark matrix and populating an associated table ofsuccessful CA configurations.

Initially, with reference to FIGS. 21a and 21b , a first processingsegment is analysed and determined to have a segmark of (127594, 85076,19761). In FIG. 21a , the matrix element at position (127594, 85076,19761) is indicated in the three dimensional segmark matrix as a solidblack dot.

Next, it is determined whether a processing segment having the samesegmark has previously been successfully compressed. This can beestablished by determining whether the corresponding element of thesegmark array—element (127594, 85076, 19761)—contains a pointer (e.g. aCAref) to a successful CA configuration. In this case, as the processingsegment is the first to be processed, the segmark matrix is empty andthe element does not contain a pointer.

If the element corresponding to the segmark does not contain a pointer,it is next determined whether any neighbouring elements contain apointer. Again, in this case the processing segment is the first to beprocessed and thus no neighbouring elements contain a pointer.

The method therefore proceeds to the next step, where the processingsegment is analysed based on different CA configurations in order toidentify a CA configuration with good prospects for compression, andcompression of the processing segment is attempted using different CAconfigurations until compression is achieved.

In this example, the first processing segment having is compressed usingCA configuration {1,1,1,9,0,0,0,0,0,0,0,0} which has a CAref of 2560.The CAref is therefore stored in the element (127594, 85076, 19761) ofthe segmark matrix.

FIG. 21b shows a table of successful CA configurations associated withthe three dimensional segmark matrix of FIG. 21a . The CA configurationtable is made up of 65536 rows, with each row corresponding to a CAref,and 65536 columns, with each column corresponding to a successfulcompression of a processing segment.

Each CAref stored in the segmark matrix is used as a pointer to thecorresponding row of the CA configuration table. Therefore, the CAref2560 points to row 2560 of the CA configuration table. The CAref of theCA configuration used to achieve compression, CAref 2560, is then storedin the CA configuration table, in the first available element in row2560, which in this case is the element in the column labelled “success1”.

Next, referring to FIGS. 21c and 21d , a second processing segment isanalysed to determine its segmark and processed to compress the segment.

In this example, the second processing segment is analysed anddetermined to have a segmark of (149685, 95624, 33762). As shown in FIG.21c , the segmark matrix element of the second processing segment atposition (149685, 95624, 33762) is indicated in the three dimensionalsegmark matrix as a solid black dot. The segmark matrix element of thefirst processing segment is indicated using a smaller black dot.

In the same way as before, it is then determined whether a processingsegment having the same segmark has previously been successfullycompressed. This can be established by determining whether the segmarkmatrix element of the second processing segment—element (149685, 95624,33762)—contains a pointer (e.g. a CAref) to a successful CAconfiguration. In this case, the segmark matrix element of the secondprocessing segment does not contain a pointer.

Therefore, it is next determined whether any neighbouring elementscontain a pointer. In this case, the segmark matrix element of the firstprocessing segment (the smaller black dot) is considered to be aneighbouring element, and this element contains a pointer to row 2560 ofthe CA configuration table. The CArefs stored in row 2560, in this casethe single CAref 2560, can then be used to attempt compression of thesecond processing segment, preferably only if the CA configurationcorresponding to CA ref 2560 is determined to fulfil at least onepredetermined processing criteria.

In this example, the CA configuration corresponding to CAref 2560 failsto achieve compression of the second processing segment. As a result,the second processing segment is analysed based on different CAconfigurations in order to identify a CA configuration with goodprospects for compression, and compression of the processing segment isattempted using different CA configurations until compression isachieved.

In this example, the second processing segment is compressed using CAconfiguration {1,1,1,7,2,0,0,0,0,0,0,0} which has a CAref of 2563. TheCAref is therefore stored in the element (149685, 95624, 33762) of thesegmark matrix.

Referring to FIG. 21d , the CAref 2563 points to row 2563 of the CAconfiguration table. The CAref of the CA configuration used to achievecompression, CAref 2563, is then stored in the CA configuration table,in the first available element in row 2563, which in this case is theelement in the column labelled “success 1”.

Next, referring to FIGS. 21e and 21f , a third processing segment isanalysed to determine its segmark and processed to compress the segment.

In this example, the third processing segment is analysed and determinedto have the same segmark as the first processing segment—a segmark of(127594, 85076, 19761). As shown in FIG. 21e , the segmark matrixelement of the third processing segment at position (127594, 85076,19761) is indicated in the three dimensional segmark matrix as a solidblack dot. The segmark matrix element of the second processing segmentis indicated using a smaller black dot.

In the same way as before, it is then determined whether a processingsegment having the same segmark has previously been successfullycompressed. This can be established by determining whether the segmarkmatrix element of the third processing segment—element (127594, 85076,19761)—contains a pointer (e.g. a CAref) to a successful CAconfiguration. In this case, the segmark matrix element of the thirdprocessing segment does contain a pointer to row 2560 of the CAconfiguration table.

In this example, the CA configuration corresponding to CAref 2560 failsto @@achieve compression of the third processing segment. As a result,the third processing segment is analysed based on different CAconfigurations in order to identify a CA configuration with goodprospects for compression, and compression of the processing segment isattempted using different CA configurations until compression isachieved.

In this example, the third processing segment is compressed using CAconfiguration CA configuration of {2,3,4,1,2,2,0,0,0,0,0,0,0,0} whichhas a CAref of 12780. As shown in FIG. 21f , this CAref is stored in thenext available element of row 2560 of the CA configuration table, whichin this case is the element in the column labelled “success 2”.

FIGS. 21g and 21h show the three dimensional segmark matrix andassociated table of successful CA configurations once they have bothbeen populated with numerous values.

As shown in FIG. 21g , neighbouring elements can be defined as elementswithin a certain volume of the three dimensional matrix space. This isindicated in FIG. 21g as a sphere surrounding a particular element,represented as a dashed circular line with a larger solid dot at itscentre. The sphere contains eight neighbouring elements, represented assmaller solid dots.

In some examples, neighbouring elements to a segmark matrix element areconsidered to be a predefined number of the closest elements in 3Dspace, e.g. the closest eight elements. In other examples, neighbouringelements can be considered to be all elements within a certain distance.The analysis of how close neighbouring elements are can be performedusing known cluster analysis methods.

Using segmarks as described above can advantageously increase the speedin which CA configurations that achieve compression are identified.

Fourier Analysis to Determine Processing Configuration

FIGS. 22a, 22b and 22c illustrate schematically steps of a method ofanalysing a processing segment using Fourier analysis to determine a bitlength L_(BP) to use in splitting up the processing segment into bitportions and/or combination arrays. Obtained Fourier coefficients can beused as guidance as to which CA configurations are likely to yield goodcompression.

FIG. 22a shows an exemplary processing segment 203, in this case made upof 512 bits, although it will be appreciated that typical processingsegments contain significantly more bits.

FIG. 22b shows a sample of the bit values in the segment, in this casethe first 55 bits, plotted as bit value versus bit position. Many bitportions have some degree of bit pattern repetition at one or morefrequencies. FIG. 22b shows that this exemplary segment includes a bitpattern which repeats every 11 bits.

FIG. 22c is a graph illustrating the results of a Fourier analysis ofthe exemplary processing segment 203. In this example, a fast Fouriertransform (FFT) has been performed on the bit values of the exemplaryprocessing segment 203. The resulting FFT amplitude is plotted againstbit length, which is equivalent the period (i.e. 1/frequency). As can beseen from FIG. 22c , a series of peaks in amplitude is typicallyobtained, which correspond to Fourier coefficients. A peak can indicatethe existence of repeating patterns at a particular bit length. In thiscase, a large peak is present at a bit length of 512, which is the totalnumber of bits in the processing segment, and therefore in this case canbe disregarded. The next peak, occurring at a bit length of 11,indicates that some degree of repetition occurs every 11 bits. As statedabove, this reflects the bit pattern which repeats every 11 bits.

Therefore, combination arrays (or bit portions) with a bit length of 11are determined to have good potential for compressing the processingsegment.

The next peak occurs at a bit length of 5.5 bits, and a subsequent peakoccurs at 3.6 bits. In this example, the nearest integer bit length of apeak amplitude is considered as a candidate bit length. Accordingly, inthis case bit lengths of 6 and 4 are also considered as candidate bitlengths, in addition to a bit length of 11. It is noted that the nearestinteger bit lengths both above and below a peak value may be considered.

In some examples, peaks can be identified by determining the average ofall obtained Fourier amplitudes and then identifying amplitude valueswhich are at least twice the average amplitude.

Preferably, any peaks occurring at a bit length of less than three bitsare ignored because compression is less likely to be successful at bitlengths of less than three bits (as explained above).

Optimising Binary Values Assigned to New CA Values (Recompression Index)

FIG. 23a is a table showing every possible 4 bit binary value from 0000to 1111, in which a recompression index is assigned to each binaryvalue. The recompression index indicates how compressible the binaryvalue is; for example binary value 0001 has a recompression index of 3,indicating relatively good compressibility, while binary value 1011 hasa recompression index of 12, indicating relatively poor compressibility.

In the examples described above, each CA value is assigned a new CAvalue, and the new CA values of different arrays are combined to givecombined new CA values. This can be seen in FIGS. 8a to 8d . Forexample, if two combination arrays are being combined, the original CAvalues 100 and 010 may be assigned new CA values 2 and 8, resulting in acombined new CA value of 10. The assigned binary label for the combinednew CA value would, according to the examples described above, be 1010(i.e. the binary representation of 10). However, in this preferredexample, the assigned binary label for each of the combined new CAvalues is optimised such that the combined new CA values associated withthe highest occurring CA values are assigned binary labels which havethe best prospects for recompression, e.g. higher levels of statisticalredundancy. This is achieved by analysing all relevant binary values anddetermining a “recompression index” for each binary value.

The recompression index is calculated by determining how many timesstrings of consecutive repeated bits occurs within the binary value. Theminimum length of repeated bits which is analysed is two bits, and themaximum is the bit length of the maximum combined new CA value beingconsidered. Considering FIG. 23a , all 4 bit binary values are listed inthe leftmost column and their respective recompression indices arelisted in the rightmost column. The 4 bit binary values are sorted basedon the recompression index, from low to high. Binary values with betterprospects for recompression have a lower recompression index, and binaryvalues with more prospects for recompression have a higher recompressionindex.

The intermediate columns show how the recompression index is calculated.The column entitled “2 Bit Reps.” lists the number of repetitions of 2consecutive identical bits in the binary value. For example, the binaryvalue 0001 contains two pairs of consecutive 0's and no pairs ofconsecutive 1's (in this example the two pairs can be shown as *00*01and 0*00*1). Therefore, the repetition of two consecutive bits isconsidered to be 2 for the binary value 0001. As can be seen from FIG.23a , the number of repetitions of three and four consecutive identicalbits is also determined for each bit value. The two, three and four bitrepetitions are then summed to give a repetition tally. As can be seen,some of the repetition tallies are the same, in which case the binaryvalues are sorted by their own relative value, from low to high. Each ofthe sorted four bit binary values is then assigned a recompressionindex, from 0 to 15. In this way, binary values with better prospectsfor recompression have a lower recompression index, and binary valueswith more prospects for recompression have a higher recompression index.

FIG. 23b is a table showing the previously described standardised binaryvalues associated with combined new CA values 0-15 along with theoptimised binary values which are assigned to combined new CA values.The optimised binary values are assigned using the recompression index,where in this example the optimised binary value assigned to the new CAvalue is the optimised binary value having a recompression index whichmatches the combined new CA value.

FIGS. 24a and 24b are equivalent to FIGS. 23a and 23b , but instead showhow binary values with a bit length of 6 are optimised. These optimisedbinary values are used for combined new CA values where the maximumcombined new CA value is between 32 and 63, e.g. a number which isnormally represented using 6 bits.

Hard-to-Compress Segments

An advantage of the present invention is that it can be used to compressdata which is usually considered hard to compress. Specifically, datawhich is more evenly distributed is harder to compress.

The present invention can vary the bit length of bit portions used toprocess data in order to capitalise on variations in distributiondepending on the bit length being used. FIGS. 25a, 25b , 26, 27 a to 27d and 28 a to 28 d illustrate how a segment can have a relatively evendistribution when divided up into bit portions of some bit lengths, buta relatively uneven distribution when divided up into bit portions ofother bit lengths.

FIG. 25a is an extract from an exemplary array which represents asegment of randomly organised and evenly distributed data. In thisexample, the array comprises 65536 elements, each element representing abyte (i.e. 8 bits), so in this instance the array includes 65536 byteshaving values between 0 and 255. It will be appreciated that the arraycould comprise any number of elements and the elements can represent anynumber of bits. FIG. 25b is a table showing the number of occurrences,within the segment, of the first 17 byte values (from 0 to 16). As canbe seen from FIG. 25b , the occurrence of each byte is similar, with theoccurrences in the table of FIG. 25b ranging from 229 to 285. Thisrepresents a reasonably even distribution.

FIG. 26 is an extract from the exemplary array of FIG. 25a written as abinary stream.

FIGS. 27a to 27d are extracts from the exemplary array of FIG. 25a ,written as a binary stream and split into bit portions having differentbit lengths. In FIG. 27a the binary stream is split into bit portionshaving a bit lengths of 3, and in FIGS. 27b, 27c and 27d the binarystream is split into bit portions having bit lengths of 4, 5 and 6respectively

FIGS. 28a to 28d are tables showing the number of occurrences, withinthe segment, of a selection of bit portion values, including the portionvalues having the highest and lowest occurrences.

In order to quantify and compare the variability (e.g. distribution) ofdata in processing segments, the coefficient of variation can be used.The coefficient of variation is given by the standard deviation of thedata divided by its mean, and is conventionally expressed as apercentage. For example, the coefficient of variation of occurrence ofeach possible byte value within a processing segment may be determined.

FIG. 28a shows the number of occurrences of each of the possible 3 bitvalues within the segment, when the segment is divided up into bitportions of 3 bits. As can be seen from FIG. 28a , the occurrence ofeach 3 bit binary value is similar, with the occurrences in the table ofFIG. 28a ranging from 21590 to 21995. The average deviation ofoccurrences from the mean occurrence of 21845 is 122.6, and thecoefficient of variation is 0.56%. Therefore, the distribution of thebinary values when the segment is split up into three bits is highlyevenly distributed.

FIG. 28b shows the number of occurrences of each of a selection of 4 bitvalues within the segment, when the segment is divided up into bitportions of 4 bits. As can be seen from FIG. 28b , the occurrence ofeach 4 bit binary value is similar, with the occurrences in the table ofFIG. 28b ranging from 7837 to 8397. The average deviation of occurrencesfrom the mean occurrence of 8192 is 140.0, and the coefficient ofvariation is 1.71%. Therefore, the distribution of the binary valueswhen the segment is split up into 4 bits is still quite even, but lessso than then the segment is split up into 4 bits.

FIG. 28c shows the number of occurrences of each of a selection of 5 bitvalues within the segment, when the segment is divided up into bitportions of 5 bits. As can be seen from FIG. 28c , the occurrence ofeach 5 bit binary value is similar, with the occurrences in the table ofFIG. 28c ranging from 3062 to 3350. The average deviation of occurrencesfrom the mean occurrence of 3277 is 72.0, and the coefficient ofvariation is 2.2%. Therefore, the distribution of the binary values whenthe segment is split up into 5 bits is less even than then the segmentis split up into 3 or 4 bits.

FIG. 28d shows the number of occurrences of each of a selection of 6 bitvalues within the segment, when the segment is divided up into bitportions of 6 bits. As can be seen from FIG. 28d , the occurrence ofeach 6 bit binary value is similar, with the occurrences in the table ofFIG. 28d ranging from 1180 to 1437. The average deviation of occurrencesfrom the mean occurrence of 1365 is 64.25, and the coefficient ofvariation is 4.7%. Therefore, the distribution of the binary values whenthe segment is split up into 6 bits is less even than when the segmentis split up into 3, 4 or 5 bits.

Accordingly, the exemplary array used in FIGS. 25a, 25b , 26, 27 a to 27d and 28 a to 28 d exhibits more even distributions when divided up into3, 4 and 8 bit portions, but less even distributions when split up into5 and 6 bit arrays. Therefore, a bit length of 6 can be used to processthe segment using the methods described herein, and compression of thesegment can be achieved.

Preferably, where data is split up into only bit portions, the standarddeviation of the occurrences of all possible bit portion values (alsoreferred to as bit portion permutations) is determined; and where datais split up into combination arrays, the standard deviation of theoccurrences of all possible combination array values (also referred toas combination array permutations) is determined,

As a general point, when analysing data from a typical file andconsidering the individual bits in the data (i.e. splitting the data upwith a bit length L_(BP) of 1), it is common to have a similar number ofoccurrences of each of the two possible bit values, 0 and 1. Therefore,at bit length 1, distribution of occurrences of across possible bitportion values tends to be very even. As the bit length increases, thedistribution will become less evenly distributed across the possible bitportion values. Furthermore, when analysing data (such as processingsegments), the number of levels identified in the bit portion values isrelated to the distribution of occurrences of bit portion (and/orcombination array) values.

This means that, as the bit length is increased, the number of levelsidentified tends to increase, up to a maximum number of levels which isequal to the bit length.

Moreover, by continuing to analyse a segment using different (generallyincreasing) bit lengths, a more uneven distribution can normally beidentified. A bit length is considered to result in a sufficientlyuneven distribution when one of the predetermined processing criteria isfulfilled. In some cases the bit length may be so long (e.g. 8 bits ormore) that the associated header size would be undesirably large. Insuch cases, the bit portions are sub-divided into combination arrayssuch that an uneven distribution remains present, but the smaller sizeof the headers associated with the combination arrays helping to reducethe overall size of the compressed file.

Other alternative or additional predetermined processing criteria may bedefined. For example, a predetermined processing criterion may be that,from analysing the coefficient of variation of bit portion (orcombination array) occurrences, the coefficient of variation exceeds athreshold. The threshold may be, for example, 1%, 2%, 5%, or 10%.

A further predetermined processing criterion may be that, from analysingthe average deviation of bit portion (or combination array) occurrencesfrom the mean bit portion (or combination array) occurrence, the numberof bits saved is more than the size of the header

In some examples, the first combination arrays tested for splitting up aprocessing segment have a bit length which tends to providecompression—for example a bit length of 6. Optionally, particularconfigurations can also be tested first, such as {3, 3, 0, 0, 0, 0},which has also been determined to frequently achieve compression.

Modifications and Alternatives

Detailed embodiments have been described above. As those skilled in theart will appreciate, a number of modifications and alternatives can bemade to the above embodiments whilst still benefiting from theinventions embodied therein.

Although it is described above that a “file” is compressed, it will beappreciated that any data may be compressed using the same methods asdescribed.

The processing segments 203 can have different sizes to one another evenwhen forming part of the same file. In one example this allows the finalsegment 203 of a file 201 to have a smaller size than the othersegments, avoiding the need to use padding bits/bytes. In other examplesthe size of the processing segments 203 can be chosen using a similarmethod to that used to choose the size of bit portions 205.

The bit portions 205 are generally all of the same size within aprocessing segment 203; however in some embodiments the bit portions 205may be of different sizes (i.e. have different bit portion lengths)within a processing segment 203.

In some alternative embodiments, each bit portion corresponds to a byte(i.e. 8 bits) of the processing segment, and there is no furtherdivision of the bit portions into combination arrays.

In some embodiments, different bit portions of the same processingsegment may be divided into different configurations of combinationarrays, which allows further exploitation of patterns, repetition and/orredundancy in a processing segment 203.

Although FIG. 3A shows the frequency analysis starting with a bitportion length of 2 bits, it can start at any bit portion length, forexample 1 bit or 3 bits.

In other alternative embodiments, the target maximum new bit portionvalue assigned to one or more bit portion values in a processing segmentis defined as follows:

${TargetMaxNewBPVal} = {L_{BP} - \left\lfloor \frac{N_{BP}^{Levels}}{2} \right\rfloor}$

Similarly, in this embodiment, the target maximum new CA value assignedto one or more CA values in a processing segment is defined as follows:

${TargetMaxNewCAVal} = {L_{CA} - \left\lfloor \frac{N_{CA}^{Levels}}{2} \right\rfloor}$

This method of calculating the target maximum new bit portion valueand/or new CA value is simpler and therefore the calculation can be mademore quickly and/or using less processing power, although the level ofcompression achieved may not always be as high.

In some alternative embodiments, the labels assigned to bit portions maybe configured to be larger than the bit portions themselves, for exampleto increase the level of encryption.

It will be appreciated by those skilled in the art that binary can bewritten right to left or left to right. For example, the binary string00010 would be considered to represent the number 2 if written right toleft, but would be considered to represent the number 16 if written leftto right. In preferred embodiments, the binary used in the methodsdescribed above is written left to right as this can make thedecompression process quicker and easier when using variable bit length.For example, writing the binary left to right can make it easier toidentify any padding bits included at the end of a processing segment.

In some alternative embodiments, no extraction information is includedin a compressed file. In some cases, the same configurations are used toprocess all processing segments, and therefore the decompressionapparatus can use information corresponding to a “static header” fordecompressing all processed segments. In some alternative embodiments,extraction information is output separately to the processed segments.

Not including a header with processed segments can be particularlyadvantageous when encrypting data, as the “static header” acts as a keyfor compression, where the key is private and only available to thecompression and decompression apparatus.

In some embodiments, for example when processing large amounts ofsimilar data, all processing segments are processed in the same way,using the same configurations, and therefore no header is guaranteed bythe compression apparatus 105, and the decompression apparatus 505decodes all processed segments 209 in the same way.

It is noted that not all permutations may be assigned labels.

In the above description, processing criteria are employed in order toselect bit lengths and/or CA configurations. In some alternativeexamples, where segments are encrypted rather than compressed, differentprocessing criteria may be used. For example, even there are no bitvalues with an occurrence of 0, a CA configuration may still beselected.

Using a variable bit length, from e.g. 2 bits up to and including allthe bits of the segment, provides great flexibility in achievingcompression. For example, if a segment is highly ordered such that thefirst half of the segment comprises one repeating pattern and the secondhalf of the segment comprises another repeating pattern, then a longerbit length can be used in order to achieve optimal compression.

Typically, each segment may comprise, for example, 65536 bytes whenusing a maximum bit length of 16 bits (n.b. 16 bits provides 65536different combinations). This segment size is preferred because itoptimises the balance between making sure the segment is small enough tofind patterns or redundancy within the data, and large enough to ensurethat the header size isn't too large in proportion to the segment.

It is noted that splitting segments up into CA configurations whichinclude only one and/or two bit arrays is less likely to achievecompression unless at least one CA value has no occurrences.

Various other modifications will be apparent to those skilled in the artand will not be described in further detail here.

What is claimed is:
 1. A method of processing data comprising an inputsequence of bits, the method comprising the steps of: (i) identifying acurrent processing configuration defining a current processing bitlength for use in processing said input sequence of bits, wherein thecurrent processing configuration defines a plurality of sub-divisions ofeach portion of a plurality of portions, each sub-division having arespective sub-division bit length, wherein a sum of said respectivesub-division bit lengths equals said current processing bit length; (ii)dividing the input sequence of bits into the plurality of portions, eachportion comprising one or more sub-divisions according to the currentprocessing configuration, wherein each portion has a respective portionbit length equal to said current processing bit length and wherein thebits in each sub-division are arranged in a respective one of a numberof possible sub-division permutations; (iii) for each of a plurality ofpossible sub-division permutations, analysing the input sequence of bitsto respectively identify how many times, within said input sequence ofbits, a portion comprises a sub-division having that possiblesub-division permutation occurs; (iv) determining whether at least onepredetermined processing criterion has been achieved by comparingresults of said analysing with the predetermined processing criterion;(v) processing said input sequence of bits based on said determiningwherein said processing comprises: when the determining determines thatthe predetermined processing criterion has not been achieved, performingat least one of: identifying a new processing configuration that isdifferent to the current processing configuration and repeating steps(ii) to (v) using said new processing configuration as the currentprocessing configuration; and ending processing of said input sequenceof bits; and when the determining determines that the at least onepredetermined processing criterion has been achieved: assigning arespective sub-division value to each of said plurality of possiblesub-division permutations; and forming a processed sequence of bits byreplacing, within said sequence of bits, bit portions comprising asub-division having bits arranged in one of said plurality of possiblesub-division permutations with a portion label based on the sub-divisionvalues assigned to that sub-division permutation.
 2. A method accordingto claim 1, wherein the respective sub-division value assigned to eachof said plurality of possible permutations is based on how many times,within said input sequence of bits, a portion comprises a sub-divisionhaving bits arranged in that possible permutation.
 3. A method accordingto claim 2, wherein the sub-division values assigned to each of theplurality of possible permutations are assigned such that sub-divisionvalues assigned to permutations which occur less often have higherlevels of statistical redundancy than the sub-division values assignedto permutations which occur more often.
 4. A method according to claim1, wherein when the determining determines that the predeterminedprocessing criterion has not been achieved and a new processingconfiguration is identified, the new processing configuration isselected in a predetermined order, for example ascending order ofprocessing bit length.
 5. A method according to claim 1, wherein theinput sequence of bits comprises a processing segment, and wherein theprocessing segment is assigned a marker which represents a distributioncharacteristic of the data within the processing segment, and whereinsaid identification of current processing configuration is based on themarker of the processing segment.
 6. A method according to claim 5,wherein identification of the current processing configuration comprisesusing the marker of the processing segment to identify a processingconfiguration which has previously been used to process a differentprocessing segment (e.g. in a different file).
 7. A method according toclaim 5, wherein the marker is determined based on mathematical analysisof the distribution characteristic of the data within the processingsegment.
 8. A method according to claim 7, wherein the marker isdetermined by: dividing the input sequence of bits into a plurality ofportions, where the bits in each portion are arranged in a respectiveone of a number of possible portion permutations; determining theoccurrence of each possible portion permutation within the inputsequence of bits; and measuring the distribution of the occurrences ofthe possible portion permutations.
 9. A method according to claim 5,wherein the distribution characteristic comprises at least one of: theaverage byte value of the data within the processing segment, theaverage change in byte value of the data within the processing segment,and the average change in byte value occurrence of the data within theprocessing segment.
 10. A method according to claim 5 marker wherein themarker comprises a multi-dimensional marker.
 11. A method according toclaim 1, wherein the processing configuration is one of a plurality ofprocessing configurations, each having a respective reference number,and wherein said processing configuration is identified by means of itsreference number.
 12. A method according to claim 1, wherein eachreference number provides a binary representation of the sub-divisionsdefined by the corresponding processing configuration.
 13. A methodaccording to claim 1, wherein said processing configuration isidentified based on Fourier analysis of the input sequence of bits. 14.A method according to claim 13, wherein said processing configuration isidentified by performing Fourier analysis on the input sequence of bitsand obtaining at least one Fourier coefficient; selecting a processingbit length based on the at least one Fourier coefficient; andidentifying a processing configuration indicating the selectedprocessing bit length.
 15. A method according to claim 1, wherein saidpredetermined processing criterion comprises whether 50% of the possiblepermutations which occur in the input sequence of bits occur at leasttwice as frequently as the other 50% of the possible permutations whichoccur in the input sequence of bits.
 16. A method according to claim 1,wherein said predetermined processing criterion comprises whether 50% ofthe possible permutations occur in the input sequence of bits.
 17. Amethod according to claim 1, wherein said predetermined processingcriterion comprises whether at least one possible permutations does notoccur in the input sequence of bits.
 18. A method according to claim 1,wherein said predetermined processing criterion comprises whether ameasure of a distribution (e.g. a coefficient of variation) ofoccurrences of the possible permutations within the sequence of bitsexceeds a threshold.
 19. A method of processing data, the methodcomprising the steps of: (i) dividing the data into a plurality ofprocessing segments wherein each processing segment comprises an inputsequence of bits; (ii) performing a mathematical analysis of aprocessing segment to determine a distribution characteristic of datawithin the processing segment and assigning at least one marker to theprocessing segment based on the mathematical analysis; (ii) identifying,based on the marker assigned to the processing segment, a currentprocessing configuration defining a current processing bit length foruse in processing a current processing segment of said data to form aprocessed segment meeting at least one predetermined processingcriterion; (ii) dividing the current processing segment into a pluralityof portions wherein each portion has a respective portion bit lengthequal to said current processing bit length and wherein the bits in eachportion are arranged in a respective one of a number of possiblepermutations; (iv) assigning a respective label to each of a pluralityof said possible permutations; and (v) forming a processed segment byreplacing, within said current processing segment, bit portionscomprising bits arranged in one of said plurality of possiblepermutations with the respective label assigned to that one of saidpossible permutations.
 20. A method according to claim 19, wherein thecurrent processing configuration defines a plurality of sub-divisions ofeach portion, each sub-division having a respective sub-division bitlength, wherein a sum of said respective sub-division bit lengths equalssaid current processing bit length.
 21. A method of processing data, themethod comprising the steps of: (i) dividing the data into a pluralityof processing segments wherein each processing segment comprises aninput sequence of bits; (ii) identifying a current processingconfiguration defining a current processing bit length for use inprocessing a current processing segment of said data to form a processedsegment meeting at least one predetermined processing criterion; (ii)dividing the current processing segment into a plurality of portionswherein each portion has a respective portion bit length equal to saidcurrent processing bit length and wherein the bits in each portion arearranged in a respective one of a number of possible permutations; (iv)assigning a respective label to each of a plurality of said possiblepermutations; (v) forming a processed segment by replacing, within saidcurrent processing segment, bit portions comprising bits arranged in oneof said plurality of possible permutations with the respective labelassigned to that one of said possible permutations; (vi) identifying anew processing configuration for use in processing a next processingsegment of said data to form a processed segment meeting at least onepredetermined processing criterion; and (vii) repeating, for each ofsaid plurality of processing segments, steps (ii) to (vi) wherein thenew processing configuration is used as the current processingconfiguration and the next processing segment of said data is used asthe current processing segment, and wherein the processing configurationused for at least one of said processing segments of said data defines adifferent processing bit length to a processing bit length defined by aprocessing configuration used for at least one other of said processingsegments of said data.
 22. A method according to claim 21, wherein eachprocessing segment is assigned a marker which represents characteristicsof the data within the processing segment, and wherein the currentprocessing configuration is identified based on the marker assigned tothe current processing segment.
 23. A method according to claim 21,wherein each processing configuration defines one of: a plurality ofsub-divisions of each portion, each sub-division having a respectivesub-division bit length, wherein a sum of said respective sub-divisionbit lengths equals said processing bit length; and an undividedprocessing portion, the bit length of which is said processing bitlength.
 24. A method according to claim 23, wherein the processingconfiguration used for at least one of said processing segments of saiddata defines a first plurality of sub-divisions having a firstcombination of sub-division bit lengths; and the processingconfiguration used for at least one other of said processing segments ofsaid data defines a second plurality of sub-divisions having a secondcombination of sub-division bit lengths; and wherein said firstcombination of sub-division bit lengths is different to said secondcombination of sub-division bit lengths.
 25. A method according to claim23, wherein the processing configuration used for at least one of saidprocessing segments of said data defines a plurality of sub-divisionshaving a combination of sub-division bit lengths; and the processingconfiguration used for at least one other of said processing segments ofsaid data defines an undivided processing portion.
 26. A methodaccording to claim 21, further comprising, between steps (v) and (vi),identifying a new processing configuration for use in reprocessing theprocessed segment and repeating steps (ii) to (v) wherein the newprocessing configuration is used as the current processing configurationand the processed segment of said data is used as the current processingsegment.